Medical Reasoning: The Nature and Use of Medical Knowledge 0190912928, 9780190912925

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Medical Reasoning

Medical Reasoning THE NATURE AND USE OF MEDICAL KNOWLEDGE Erwin B. Montgomery, Jr., MD MEDICAL DIRECTOR GREENVILLE NEUROMODULATION CENTER GREENVILLE, PENSYLVANIA, UNITED STATES PROFESSOR OF NEUROLOGY DEPARTMENT OF MEDICINE MICHAEL G. DEGROOTE SCHOOL OF MEDICINE AT MCMASTER UNIVERSITY HAMILTON, ONTARIO, CANADA

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1 Oxford University Press is a department of the University of Oxford. It furthers the University’s objective of excellence in research, scholarship, and education by publishing worldwide. Oxford is a registered trade mark of Oxford University Press in the UK and certain other countries. Published in the United States of America by Oxford University Press 198 Madison Avenue, New York, NY 10016, United States of America. © Oxford University Press 2019 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, without the prior permission in writing of Oxford University Press, or as expressly permitted by law, by license, or under terms agreed with the appropriate reproduction rights organization. Inquiries concerning reproduction outside the scope of the above should be sent to the Rights Department, Oxford University Press, at the address above. You must not circulate this work in any other form and you must impose this same condition on any acquirer. Library of Congress Cataloging-​in-​Publication Data Names: Montgomery, Erwin B., Jr., author. Title: Medical reasoning : the nature and use of medical knowledge / Erwin B. Montgomery, Jr. Description: New York, NY : Oxford University Press, [2019] | Includes bibliographical references and index. Identifiers: LCCN 2018018343 (print) | LCCN 2018019011 (ebook) | ISBN 9780190912932 (online content) | ISBN 9780190912949 (updf) | ISBN 9780190912956 (epub) | ISBN 9780190912925 (cloth : alk. paper) Subjects: | MESH: Logic | Philosophy, Medical | Decision Making | Thinking | Metaphysics Classification: LCC R723 (ebook) | LCC R723 (print) | NLM W 61 | DDC 610.1—dc23 LC record available at https://lccn.loc.gov/2018018343 This material is not intended to be, and should not be considered, a substitute for medical or other professional advice. Treatment for the conditions described in this material is highly dependent on the individual circumstances. And, while this material is designed to offer accurate information with respect to the subject matter covered and to be current as of the time it was written, research and knowledge about medical and health issues is constantly evolving and dose schedules for medications are being revised continually, with new side effects recognized and accounted for regularly. Readers must therefore always check the product information and clinical procedures with the most up-​to-​date published product information and data sheets provided by the manufacturers and the most recent codes of conduct and safety regulation. The publisher and the authors make no representations or warranties to readers, express or implied, as to the accuracy or completeness of this material. Without limiting the foregoing, the publisher and the authors make no representations or warranties as to the accuracy or efficacy of the drug dosages mentioned in the material. The authors and the publisher do not accept, and expressly disclaim, any responsibility for any liability, loss or risk that may be claimed or incurred as a consequence of the use and/​or application of any of the contents of this material. 9 8 7 6 5 4 3 2 1 Printed by Sheridan Books, Inc., United States of America

For Lyn Turkstra, who saved my life in so many ways and for those clinicians who did their utmost when it was not forced or popular.

CONTENTS Preface  xiii Glossary of Concepts  xxi Companion Website  xxxix



1. Introduction: Places to Explore the Ramifications of Uncertainty  1 Deduction and Its Derivatives  2 Probability and Statistics  6 Extra-​Logical Considerations  10 Induction  12 The Discipline of Logic  15 Origins of Ideas as Hypotheses  16 Rationalist/​Allopathic Medicine Versus the Empirics  17 Science and Scientism  19 Perspective  20



2. What Are We to Make of Reasoning in Modern Medicine?  21 Certainty, Knowledge, and Understanding  22 Track Record on Beneficence  25 Reproducibility in Biomedical Science  26 Approach in this Text and an Emphasis on Logic in Its Widest Connotation  27 Evolutionary Epistemology and Logic  27 Use and Misuses of Logical Errors  29 Applied Epistemology  29 Descriptive, How Things Are, Versus Normative, How Things Should Be  30 The Dichotomization of Medicine Between Science and Art  30 Logic and Its Extensions Versus Science  32 Medicine Beyond the Realm of Science but Within the Realm of Epistemology, Logic, and Logic’s Extensions  34



3. Epistemic Challenges and the Necessary Epistemic Responses  36 The False Choice Between Universal Scientific Judgment and Particular Common Sense Judgment  37 The Myth of the Inevitability of Certainty  40 Clinicians’ Obligation to Reason  40 The Evolution of Medical Reasoning and Misreasoning  41

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The Importance of Philosophical Analyses  43 Future Areas of Study in Logic  44



4. Medical Epistemology: The Issues  45 Case  45 Deduction  46 The Limits of Deduction  47 The Patient Imperative  48 Dichotomization Based on Statistical Significance  49 What Physician A and What Physician B Thought  50 Comparing Apples and Oranges  52 Facts Are Insufficient  54



5. Deduction, Induction, and Abduction: The Basics  56 Case  56 Logical Argumentation  57 Consequence of the Failure to Uphold the Principle of the Excluded Middle  59 The Problematic Nature of the Throat Culture  61 Sensitivity, Specificity, and Positive and Negative Predictive Values as Logical Forms  63 Deduction  65 Fallacy of Confirming the Consequence  66 Rescuing the Hypothetico-​Deductive Method  67 Abduction  69 Principle of Transitivity and the Fallacy of Pseudotransitivity  69 Induction  71 Induction and the Scientific Method  73



6. Evolution of Medical Reasoning  75 Ascendency of Allopathic (Scientific) Medicine  76 The Inverse Problem  77 Historical Approaches to Diagnosis  78 Medicine and Science  79 Diagnosis and Treatment  80 Evolution of Medical Science  81 The Persistence of Galenic Ideas with the Advancement of Medical Science  84 Dominance of Mechanistic Theories of Physiology and Pathophysiology  87 History of Medical Abduction  87 Institutionalization of Scientific Medicine and Further Reinforcement of Abduction  89 A Different Notion of Science  91

Contents



7. Variability Versus Diversity in Variety: The Epistemic Conundrum and Responses  93 Variability Versus Diversity  93 The Human Epistemic Condition  96



8. The Meaninglessness of the Mean  98 Medians and Quartiles  102 Cumulative Percentage (Probability) Function  104 The Metaphysical Notion of the Mean (and Median)  105 Different Types of Means  107



9. The Value of Statistical and Logical Thinking  108 Wired for Intuition?  109 Statistical and Epistemic Thinking  111 Are Randomized Controlled Trials Privileged?  113 Undersampled and Biased Experience and Induction  114 Non–​Evidence-​Based Medicine Methods of Medical Reasoning  116

10. The Centrality and Origins of Hypotheses  118 The Importance of Hypotheses  119 Origins of Hypotheses  120 Hypotheses and Postmodernism  121 Hypothesis Generation in Medical Decisions  123 Origin of Hypotheses from Medical Science  124 Role of Presuppositions or Metaphors in the Origin of Hypotheses  125 Diagnostic and Statistical Manual V (DSM-​5): Modern-​Day Battle of the Allopaths and Empirics  126

11. Necessary Presuppositions: The Metaphysics  128 The Centrality of Metaphysics (Properly Defined)  128 Allopathic Medicine and Science  130 Role of Theory in Shaping Observation  131 Aristotle’s Notion of the Contraries  132 The Triumph of Allopathic Medicine (the Modern Doctor of Medicine and Doctor of Osteopathic Medicine) and Its Presuppositions  133 Rationalism (Reductionism), Empiricism, and Dogmatism and Their Presuppositions  133 Dealing with Variety and Its Consequent Uncertainty  135 Allopathic Medicine and Reductionism  136 Cell Theory, the Rise of Pathology (Particularly Histopathology), and Pathological–​Clinical Correlations of William Osler  138 Mereological Fallacy  141 The Conundrum Is the Result of the Inverse Problem  142 Neurology and the Neuron Doctrine  143 A Case Study  144

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12. The False Notion of Intention, Choice, and Inhibition  150 One-​Dimensional Push–​Pull Dynamics  150 Methodological Reduction Creating New Ontology  152 Misperception of Inhibition  154 Potentiality  157

13. The Role of Metaphor  160 Metaphors in Medical and Scientific Knowledge and Understanding  161 Metaphors Leading to Errors in Medical and Scientific Knowledge and Understanding  162 Metaphors and Metonymies as Structuring Observation  163 Nonlinguistic Metaphors  164 Reductionism as a Process Metaphor  166

14. Dynamics  167 The Necessity of Addressing Dynamics, but How?  168 Continuing Concerns for Telos  170 One-​Dimensional Push–​Pull Dynamics  171 Understanding Dynamics Through Metaphor  173 Chaos and Complexity  174

15. Medical Science Versus Medical Technology  178 Medical Science, Medical Technology, or Both?  179 Lessons from the Past  180 A Special Case of Technology Looking for a Question  181 Mathematics as a Technology  182 Science for Its Own Sake, Technology for the Sake of Others  183 Experimentalism and Science as Technology  183

16. Irreproducibility in Biomedical Science  186 The Magnitude of the Problem  186 Scope of the Issue  188 Information Loss and Irreproducibility  189 Areas to Explore  191 Reconstruction: The Other Half of Reductionism and Relevance to Reproducibility  194

17. Medical Solipsism  195 Knowledge in Medicine  195 Nature of Solipsism  197 The Use of Consequence to Resolve the Solipsist’s Advantage  199 The Solipsism of Evidence-​Based Medicine  200 The Solipsism of US Food and Drug Administration Approval  202 The Solipsism of Balkanized Medicine  203

Contents

18. Critique of Practical and Clinical Medical Reasoning  204 Distinctions Without Distinctions?  205 Clinical Intuition  209 Fundamental Limits of Science and Scientific Reasoning Necessitating Practical Reasoning  213 Clinical Meaningfulness  215 Clinical Assessment  217 The Diagnostic/​Therapeutic Scheme  219 Difference Between What Can Be Done and What Should Be Done  220

19. A Calling to Be Better than Ourselves  222 Malpractice and Accountability  223 Peer Review  224 Too Few to Fail  224 Ethical Principles and Moral Theory  225 References  227 Index  237

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PREFACE Writing and publishing this text entail risks because a large receptive audience cannot be assumed a priori. The topic is one seldom addressed, and therein lies the challenges. It is not as though this text addresses a widely recognized and greatly appreciated need. It does not offer an answer to the cure for any disease for which there is not one already, nor for new ways to relieve incurable diseases. It offers no new laboratory tests or imaging studies. So, what does it offer? The attempt here is to exercise and thereby sharpen the most important tool any clinician or scientist can wield—​their brains. Some may dismiss such an effort, finding it unnecessary or even demeaning. Yet clinicians are human and as prone to errors as anyone else. This can be embarrassing: witness the 83% of radiologists who did not recognize the image of a gorilla in a computed tomography (CT) scan of the chest despite looking directly at it (Drew et al., 2013), and, as such, clinicians have a duty to themselves, the profession, and patients to be cognizant of the errors to which the human flesh is err as the prerequisite to avoiding them. It is likely that every clinician and scientist is concerned about the sensitivities, specificities, positive and negative predictive values, and consequences of failure to recognize false positives and false negatives in any laboratory or imaging test. Every clinicians’ and biomedical scientists’ mind may not be considered a tool, but the consequences of the mind’s use for patients are just the same as the application of any tool or device. The consequences of a false positive or false negative resulting in a wrong diagnosis or treatment is the same whether the false positive or false negative was the result of a blood test or the clinician’s mind. Does not the similarity in consequence, if not in operation, between the use of tools versus the clinician’s mind warrant the same degree of concern and thus scrutiny? The affirmative answer motivates this book. This text is a critique of medical reasoning, but the term “critique” is taken in its philosophical connotation, which means “rigorous analysis.” It is not a value judgment in any ethical or moral sense. Rather, terms of “productive” or “counterproductive” to the intended purpose are used. Whether productive medical reasoning is demanded or counterproductive reasoning countenanced is an ethical question resolved in the context of the controlling moral theory. Thus, good or bad medical reasoning depends on ethics. However, this is not as relative as some may think because there is a general principle—​common xiii

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morality (Beauchamp and Childress, 2013)—​induced from the consideration of reasonable persons, that provides an operationalization of good and bad. Throughout the text, I will present examples of what generally would be considered bad medical reasoning. These are raised solely to demonstrate the importance of a critical analysis of medical reasoning and to highlight that complacency is not an option. The very large majority of clinicians that I have encountered in more than 40 years of practice are very smart and talented, yet misadventures in medical reasoning are not rare. So, whence the bad medical reasoning? Caring for humans is difficult because humans are complex; consequently, manifestations of health and disease present with great variety. The variety presents an epistemic conundrum in gaining new knowledge. In response, specific epistemic choices must be made. Each choice renders knowledge problematic and therefore uncertain. The needs of patients force actions even when knowledge is uncertain. Certainty in medical knowledge depends on valid deductive logic with true premises and valid arguments. However, gaining medical knowledge requires the use of logical fallacies. Furthermore, probability, and thus statistics, is founded on logical fallacies, and statistical errors often derive from the misuse of these inherent logical fallacies. The judicious use of logical fallacies leads to new knowledge and optimal care; injudicious use leads to failures and errors. For example, it will be demonstrated that randomized clinical trials, virtually the sine qua non of evidence-​based medicine, rest on the logical Fallacy of Four Terms. The hypothetico-​deductive approach to diagnosis and treatment rests on the logical Fallacy of Confirming the Consequence, which risks confirmation bias. However, no reasonable clinician or scientist would jettison randomized controlled trials or the hypothetico-​deductive approach, and that is not the argument in this book. Rather, this book attempts to understand the dangers of misusing the Fallacy of Four Terms in order to strengthen the utility of randomized controlled trials and the hypothetico-​ deductive approach. Rarely in my experience are there discussions of logic in medicine or biomedical science. To be sure, numerous publications relate to the logic of medicine, but the term “logic” is more or less generic in the sense of a method or algorithm, and it is used more often in a descriptive (how things appear to work) rather than a in normative (how things should work) sense. Thus, many animals can be described as having logic but not likely the type of deductive logic first championed by Aristotle. Indeed, the type of logic common to most animals (including humans) can be held synonymous, roughly, with cognitive function, and human medical reasoning can be understood in terms of cognitive functions and errors in terms of cognitive biases (see, e.g., the work of Norman and colleagues, 2006). Yet these, too, can be understood as variations of deductive and inductive logic. Some have contrasted analogical reasoning as being somehow different from deduction (and its variants) and

Preface

induction. When understood as the Fallacy of Pseudotransitivity derived from the Principle of Transitivity in deductive logic, then analogical reasoning is an extension of deductive logic. Understanding inductive and deductive logic and the extensions to logical fallacies, probability, and statistics is critical to nearly every aspect of medical reasoning. Some authors have attempted to formulate medical reasoning in deductive terms, and attempts have been made to extend the utility of deductive logic to the complex and often imprecise realm of medicine (for example, through the use of fuzzy logic). However, I have not encountered discussions of a deductive logic approach to medical reasoning where logical fallacies are embraced rather than avoided, the necessity to embrace will be demonstrated. If true that it is necessary to embrace logical fallacies, the relative novelty of the concept, at the very least, offers a different perspective that perhaps could be helpful. This notion of logic, expanded to the judicious use of fallacies, is evolutionary and organic. Because these fallacies arise from the need to treat individual, specific patients, they are, hence, organic. The epistemic nature of medical care centers on the need for certainty but also utility. The absolute certainty of deductive logic is very limited in utility. Deductive logic derives its certainty from the severe constraints placed on possibilities entailed by the Principle of the Excluded Middle. Yet questions confronting clinicians seldom have only true or false answers. Confronted by great variety in the expressions of health, disease, and disorder, the epistemic question is whether the variety derives from variability or diversity. Variability suggests variations around some economical set of canonical forms. Medical knowledge and decision-​making can be made more economical by addressing the canonical forms rather than individual instantiations. Reductionism is one example. Diversity, by contrast, argues that no such economization is possible and that each patient is a new and independent phenomenon—​taken as de novo. The epistemic conundrum resulting from the variety of manifestations has driven different approaches to medicine since the ancient Greeks and continues to do so to this day. Mechanistically, humans are incredibly complex, far more complex than would be inferred from their observable behaviors. This notion is inherent in the concept of medical syndromes as distinct from medical diagnoses. Congestive heart failure is a syndrome of specific symptoms and signs that follow from any number of different diagnoses. This means that, at the level of the patient’s direct manifestations, one cannot determine which of the diagnoses is responsible for the individual patient’s syndrome. This lack of one-​to-​one correspondence between the symptoms and signs to a single diagnosis presents the epistemic conundrum of the Inverse Problem, meaning one cannot infer the specific diagnosis from the manifestations of the syndrome. The analogue to the Inverse Problem in logic is the Duhem–​Quine thesis, which states that, should an argument result in a false conclusion, one cannot

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know which of the component premises were false or which proposition was invalid. The instantiation in probability theory is the Gambler’s Fallacy. These issues are explored in detail throughout the book. Induction, an inference to general principles based on a set of observations, is another source of knowledge, whether of medical science or a patient’s unique condition. Yet induction presents epistemic conundrums. These include the Fallacy of Induction and the A Priori Problem of Induction, discussed in detail in later chapters. Some authors, such as Groopman (2007), fail to appreciate the nature of logic and do a disservice by creating straw-​man arguments (which is casting one’s opponent as obviously flawed or failed, independent of the argument, in order to advance one’s own position). Groopman describes a brilliant pediatric cardiologist who thought he was being logical, with the result being a near catastrophe. Basically, the conclusion in Groopman’s work was not to trust logic, but, as will be seen, logic—​when used properly—​is the only source of confidence in decision-​making. Groopman’s case involved a pediatric patient with severe narrowing of the mitral valve of the heart and a hole in the wall between the atrium of the two halves of the heart. The result was that blood in the left half of the heart would flow to the right side and not go through into the left ventricle and then on to the aorta to supply the body because higher blood pressures were required to push the blood through the narrow mitral valve and out to the body. The cardiologist reasoned that if one closed the hole in the wall, there would be higher blood pressure in the left side of the heart because the blood pressure would not be reduced by the blood going into the right side of the heart. The cardiologist was quoted as saying “It has to be right, correct? It is very sound logic. But it’s wrong.” The child got worse when the hole in the wall of the heart was closed. The explicit implication was to not trust logic. The reasoning here was illogical, as will be discussed in Chapter  5. The failure of the argument has nothing to do with logic, but is instead due to a false proposition (closing the hole in the wall would increase the blood pressure in the upper left half of the heart), for which the cardiologist is at fault, not logic. Even the soundest logic does not guarantee true conclusions if the premises are false. If someone chooses to use a hammer to saw a piece of wood, it is not the fault of the hammer that the piece of wood is shattered. Thus, attributing the cardiologist’s argument to logic is faulty and, if anything, demonstrates the clear need for clinicians to understand and apply logic appropriately. Rapid advances in biological sciences, particularly since the early 1800s, have had and continue to have profound impacts on medical reasoning but perhaps not in a manner commonly thought of today. As will be seen, it was not an achievement of biological science in improving medical care that led to the current dominance of allopathic (modern) medicine. Rather, it was allopathic medicine’s adoption of scientism, following on the heels of advancing

Preface

biomedical science, that drove the process. “Scientism” here refers to the metaphysical stance (perhaps best described as faith) that we can “science” our way to certainty. This history is reviewed in subsequent chapters. Fortunately, there is a long history of concern with efforts to understand reasoning going back to the ancient Greek philosophers. Their knowledge and experience can help clinicians and biomedical scientists, and, correspondingly, philosophical terminology is used. Many philosophical terms are convenient shorthand references to important and complex concepts and are no different from the shorthand of medical and scientific technology. Understanding the general form of logic, its extensions into judicious fallacies, and probability and statistics allows one to spot potential trouble in many contexts. In any medical discussion, when a decision is recognized as a specific instantiation of the general logical form if a implies b is true, b is true, therefore a is true, then that medical decision is immediately suspect because it represents the Fallacy of Confirming the Consequence. It does not matter exactly what a or b is. Philosophers (and others whose professions involve the analysis of reasoning) can make important contributions to understanding and teaching medical reasoning to clinicians and biomedical scientists. To do so, it is important that each appreciates and converses in each other’s language. Clinicians may become philosophers in their own right. As the philosopher Bertrand Russell said, “To teach how to live without certainty, and yet without being paralyzed by hesitation, is perhaps the chief thing that philosophy, in our age, can still do for those who study it” (Russell, 1940). The fear is that this text may be greeted like so much unsolicited advice. Such advice often is disturbing because it presupposes a problem that the advice is intended to rectify. As the receiver of the advice never asked for it in the first place, the unsolicited advice is perceived as an insult, not only because there is a problem for which the receiver is responsible, but also because the receiver did not have the sense or presence of mind to recognize the problem. The majority of clinicians will tolerate being told that their diagnoses or treatment recommendations are a mistake, but it is a rare individual who tolerates being told that the way he or she thinks led to the mistake. Yet the ubiquity of medical errors and the lack of reproducibility in biomedical research suggest that something is amiss and that it is important to at least consider the possibility of misreasoning (Chapter 2). As will be addressed, medicine is not synonymous with science, and conflating the two produces potential sources of errors in medical reasoning. Indeed, the very methods, such as statistical inference and reductionism used in science, result in an irretrievable information loss of precisely the type needed to apply science to the care of individual patients. It is a matter of physics (based on information theory and the Second Law of Thermodynamics), as will be discussed.

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Ultimately, as will be seen, uncertainty can only be managed—​it cannot be eliminated. Scientific and statistical methods only convey an impression of reducing uncertainty. A reduction in apparent uncertainty in one context only surfaces as uncertainty elsewhere. Managing uncertainty properly requires a full and clear understanding of the nature of uncertainty and the nature of knowledge, a concern tackled by the philosophical discipline of epistemology. The reader may note some degree of redundancy in the text. This is necessary and parallels experience in biomedical ethics. One can know the ethical principles and moral theories, but how these combine to resolve ethical questions requires discussion in the specifics. The theme here is that logic and its necessary extensions of judicious fallacies, probability, and statistics form the economical set of principles that can be recombined to understand specific cases. Importantly, it is only in the context of many specific cases that the full dimensions of logic and its extensions can be appreciated. It is worth drawing attention to two features that may help readers not familiar with logic, epistemology, logic, probability, and statistics. First, a Glossary of Concepts (presented next) provides a brief introduction to a variety of concepts addressed in the book. Some readers may find it helpful to read the Glossary of Concepts in preparation for reading the main text. Also, two online appendices provide brief introductions to logic and to probability and statistics; they are available online at www.oup.com/us/medicalreasoning. From what vantage do I  come to recognize and understand the errors of medical reasoning? It certainly did not come from my undergraduate studies in biochemistry, nor from medical school, residency, or the fellowship that followed. As an assistant professor in the Department of Neurology at Washington University in St. Louis, I  took graduate courses in philosophy, particularly in epistemology. What I  learned from that experience is that, in philosophy, nothing was above or beyond debate. Through practice in graduate school and subsequently, I  acquired some skill to unpack arguments, which is deconstructing arguments to their fundamental premises, particularly assumptions, presuppositions (implicit assumptions), propositions, and logical structures. I had the distinct advantage of prior efforts extending back through thousands of years by a continuous tradition of intellectual rigor in philosophy. It is to my professors of philosophy from 1981 to 1990 at Washington University in St. Louis that I owe a very large debt of gratitude. What drove this writing are my many years of attending on the teaching wards and seeing the bafflement on the faces of very intelligent women and men during their later training in medical school when confronted with errors in medical reasoning that they had seen as standard practice elsewhere. Some students battled the confusion and sought to understand; unfortunately, there was so little time, with so much wrong thinking to be undone and new intellectual rigor to instill. Also, there was the tyranny of trying to be a success at my

Preface

mainstream academic career, which provided little time for efforts in the epistemology of medical reasoning. In July 2014, I  was offered a unique opportunity to become the medical director of the Greenville Neuromodulation Center and the Greenville Neuromodulation Scholar in Neuroscience and Philosophy at Thiel College in Greenville, Pennsylvania. The fact of being rather senior with nothing to prove to anyone and more time to pursue my latent philosophical interest allowed me to take up this effort to understand medical reasoning, as it is and how it might be. Teaching students in the small liberal arts college provided a valuable challenge and sounding board. Also, I received an unimaginable stroke of good fortune to meet and interact with Dr. Arthur “Buddy” White, a true philosopher and chair of the Department of Philosophy at Thiel College. His passion for philosophy was contagious and would heat to flame even the dampest kindling. I am indebted to the leadership of Greenville Neuromodulation Center for the opportunities and support for this effort. In the efforts that follow, whether I  am right or wrong in my analyses and recommendations is of little consequence. If this text sparks a discussion among others, one that continues to evolve and perhaps one day change the way clinicians and biomedical scientists think, then that would be a satisfaction that no one can deserve but nonetheless is a blessing. Finally, thanks to Erwin B. Montgomery III, PhD and Melissa Revell who translated from my poor prose. —​Erwin B. Montgomery, Jr., MD

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GLOSSARY OF CONCEPTS This glossary is intended to be a ready reference for novel concepts or novel variations on established concepts as they are encountered while reading the book. Alternatively, the reader may first wish to review these concepts to become familiar with them prior to encountering them in the main text of the book. This glossary presumes some familiarity with logic and probability. Those wishing to refresh this knowledge might first review Appendices A and B, available at www.oup.com/us/medicalreasoning. Abduction—​a form of reasoning that resembles deduction, but, in reality, it is the logical Fallacy of Confirming the Consequence, which is of the form if a implies b is true and b is true then a is true (see Fallacy of Confirming the Consequence). For example, one might reason that if patient A had strep throat then the patient should have a sore throat, fever, and an exudate in the throat; the patient has a sore throat, fever, and an exudate; therefore, the patient has strep throat. However, the patient could have a sore throat from any number of other causes. The hypothetico-​deductive approach is an example of abduction in allopathic medicine (see Hypothetico-​Deductive Reasoning and the Scientific Method). Actor/​Action Distinction—​the problem of inferring the mechanisms of action from observations of the actor. Originally derived from an analysis of meaning, the question was posed whether an expert actor, such as a Shakespearian actor, flawlessly reciting Shakespeare’s lines, actually understood what Shakespeare meant or intended. By extension, observation of the actor alone cannot convey an understanding of what Shakespeare meant or intended. Thus, observations of expert clinicians may not reveal the actual modes of reason, particularly as the expert clinician may be unaware of the reasoning modes used. Allopathic Medicine—​also called rational or regular medicine. This program or school of medicine constructs diagnosis and treatment on the basis of an economical set of principles from which an explication of the individual patient is reconstructed. As such, allopathic medicine was well poised to take advantage of the emergence of modern medical science with emergence of the cell theory, the germ theory, histopathology, and microbiology. Allopathic medicine stands in contrast to the Empirics, who held that the health or disease of individual persons could not be explained on xxi

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an economical set of fundamental principles but rather that each patient has to be taken on as an epistemic problem in his or her own right, de novo. Analogical Reasoning—​a mode of reasoning based on analogies; for example, treatment of patient A with disease X is based on the similarity of patient B, thought to have disease X, and her response to treatment Y. Use of analogy rather than synonymy allows for accommodation of the great variability among patients, their health, and the manifestation of their dis­ ease. A clear example is in the off-​label use of medications in the United States. Similarity of patient A to patient B for which treatment Y has been approved or recommended becomes justification for the use of treatment Y for patient A. Some argue that analogical reasoning is of a different kind than logic. However, from the perspective of evolutionary logic (see Evolutionary Logic), analogies are an extension of deductive logic, particularly the concept of the Fallacy of Pseudotransitivity (see Fallacy of Pseudotransitivity), which derives from the logical Principle of Transitivity. A Priori Problem of Induction—​induction typically involves generalizing a principle from a set of observations; for example, every raven seen is black, therefore all ravens are black. The A  Priori Problem of Induction asks, “What are the rules, implying prior knowledge, by which a specific set of birds is selected to form the set of ravens from which the inference based on commonality—​that of being black—​is derived?” Note that if blackness is a rule, then the induction becomes a tautology conveying no new knowledge. Nor can the rules be such that crows or black swans would be included. Thus, induction requires some prior knowledge or circumstance. The A Priori Problem of Induction affects and limits pattern recognition as a means of medical decision-​making (see Pattern Recognition). Baconian Science—​a version of science that promulgates itself as being strictly empiric and arrived at by experimentation. The motto of the Royal Society Nullius in Verba (Take No One’s Word for It) typifies this approach. Taken to its extreme, no claim can be demonstrated by derivation or extrapolation from prior principles and initial conditions. For example, mathematical proofs would not be acceptable as evidence in support of a scientific claim. Baconian science tends to typify the version of science claimed by medical science and dominating medical reasoning. Bayes’ Theorem—​see “Hoof Beats and Zebras” (Bayes’ Theorem) Cartesian Science—​an alternative version to Baconian science (see Baconian Science) that emphasizes rationalism where new “scientific” knowledge is synthesized from fundamental principles. For example, one does not need to demonstrate Newton’s laws of motion empirically (inductively) to determine an astronomical anomaly that subsequently proved to be the planet Pluto. By the same token, one does not need to demonstrate empirically the therapeutic effect of penicillin in the event that the eventual use of penicillin may be considered.

Glossary of Concepts

Causal Syllogism—​a derivation from the practical syllogism, which is a variant of the deductive syllogism where the state-​of-​being linking verb is replaced by the action verb, in this case cause. For example, the typical syllogism is of the form all a’s are b’s, and c is an a, therefore c is a b. One can see the certainty and utility if a is disease A (sample of such patients), b is effec­ tive treatment B, and one’s patient is c. However, not all circumstances are amendable to syllogistic deduction. For example, one might want a to be as symptoms of a disease, b, and c is a patient with symptoms a. Here the relation is one of cause, and the syllogism becomes all a’s are caused by dis­ ease b, one’s patient (c) has symptom a, therefore one’s patient has disease b. This is an example of a causal syllogism that does not have the certainty of a syllogistic deduction. Causational Synonym, Principle of—​see Principle of Causational Synonymy. Chaos and Complexity—​originally demonstrated in mathematical systems and now with growing evidence in physical systems, chaos and complexity refer to systems that cannot be defined definitively, and hence any future behavior cannot be predicted. Indeed, it is the unpredictability of chaotic and complex systems that is their main defining feature. Additionally, such systems are highly dependent on the initial conditions from which any system would start. Chaotic systems are relatively simple systems, but their interactions are highly nonlinear. For example, the state equation y = x is linear in that a doubling of x leads to a doubling of y. However, equations such as y = −xx are nonlinear. Complex systems may have simpler dynamics, but their sheer number of interactions makes the outcomes difficult to predict. These systems present a problem for most current statistical approaches to make “sense” or inferences. To the degree that human biology is chaotic or complex, the application of traditional statistical analysis likely is unproductive, at the least, and misleading, at the worst. Clinical Judgment—​see Practical Judgment. Complexity and Chaos—​see Chaos and Complexity. Contraries—​originating from Aristotle’s Physics (2001), it is a method used to simplify understanding of the interaction between phenomena (dynamics) and the phenomenon itself. Simplification is achieved by reducing different dynamics to a dynamic that is one-​dimensional. For example, consider a gray scale that goes from white to black. The question is, “How many shades of gray are there?” The answer depends on the resolution of the device used to measure “grayness,” but, potentially, there are an infinite number of grays. However, such a position would obligate one to an infinite number of elements (ontologies)—​shades of gray—​such that managing them in any science of the gray scale would become very complex. Alternatively, one can take the position that there are just two shades of gray—​black and white—​that are the extremes of the continuum. Thus, the potential infinity of ontologies reduces to just two. The dichotomization

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of a phenomenon alone as a single dimension, often characterized by a push–​pull dynamic, is ubiquitous in medicine and may well be the source of error. Cookbook Medicine—​a term used to describe the practice of medicine that directs patient diagnosis and management to a predefined algorithm. However, in typical use, it refers to an economical set of algorithms that are expected to be optimal for every patient. This is analogous to the dogmatic school of medicine originating with the ancient Greeks (see Dogmatics). Those using the term “cookbook medicine” in the usual pejorative sense often do so based on skepticism. The problem is often that skepticism reflects an unfounded bias, perhaps because of a specific tradition or, in a polemical sense, that if clinicians can be considered equivalent to algorithms, then a reduction in the clinician’s autonomy is threatened. Deduction—​ a method of argumentation subsumed in the discipline of logic. The value of deductive logic is that, given true premises and valid propositions, then the conclusions must be true. This provides the greatest level of certainty possible. However, the degree of certainty is related reciprocally to the utility of such arguments. Deductions typically resolve down to tautologies (see Appendix A) and thus do not lead to new knowl­ edge. Deduction is “truth preserving” but not “truth generating.” A significant problem in medicine is that reasoning often is described as deductive and thus trades on the confidence that can be derived from true deductive arguments. But they are not strictly deductive and, in reality, do not have the certainty of deduction with true premises and valid propositions. Examples include the hypothetico-​deductive approach to diagnosis and the scientific method, which embody the logical Fallacy of Confirming the Consequence (see Fallacy of Confirming the Consequence). Differential Diagnosis—​ a list of possible diagnoses causal to the patient’s phenomenological syndrome. Discussion of the differential diagnosis is made to contrast it with the actual diagnosis. The distinction is critical because the differential diagnosis is the starting point of medical diagnostic decision-​making, and the diagnosis is a consequence. Proceeding directly to a diagnosis without first considering a differential diagnosis risks misdiagnosis, which stems from the Fallacy of Limited Alternatives (see Fallacy of Limited Alternatives) in the context of the Inverse Problem (see Inverse Problem). Dogmatics—​a school of medicine at least since the ancient Greeks that held that patients should be managed by direct referral to accepted medical texts—​possibly a form of cookbook medicine (see Cookbook Medicine). “Don’t Change Too Many Things at Once Lest You Get Confused”—​related to admonition in medicine often raised as an example of clinical or practical reasoning. Sometimes the use of clinical or practical reasoning is cited as evidence that medicine cannot be scientific or logical and that extra-​logical

Glossary of Concepts

reasoning is necessary. This practical admonition reflects the epistemic condition of the Inverse Problem (see Inverse Problem). Duhem–​Quine Thesis—​a thesis related to finding the cause of a failed argument. In any deduction, a false conclusion necessarily means that one or more premises are false or one or more propositions are invalid. The problem is that, within the providence of the argument, there is no way to tell which premise is false or which proposition is invalid. Economical—​in the context of this book, economical does not refer to anything monetary (except as specific designated exceptions). Rather, it is used in a quantitative sense and relates to the ratio of different phenomena that must be explicated and the number of underlying principles necessary for each explanation. Empirics—​also called irregular medicine—​an approach or school of medicine that stands in contrast to allopathic medicine (see Allopathic Medicine). The fundamental premise of the Empirics is that an explication of any individual patient on the basis of an economical set of principles, such as those related to physiology and pathophysiology, is not possible. Thus, the only means to treatment is to analyze the specific individual symptoms and signs manifest by each patient and direct specific treatments to the specific symptoms and signs. In homeopathy, this approach is reflected in the saying “like cures like.” Epistemic Condition—​this condition relates to the fundamental dilemma that confronts the acquisition and use of human knowledge, particularly as it relates to medical decision-​making. The greatest certainty in knowledge comes from logical deduction (see Deduction), yet deduction does not yield new knowledge, which is crucial to any empiric discipline such as medicine. Rather, logical fallacies must be employed in order to gain new knowl­ edge but at the loss of certainty. The incorporation of judiciously used logical fallacies defines evolutionary epistemology and evolutionary logic. Methods and approaches have been developed to minimize the loss of certainty, such as probability and statistics. The risk for uncertainty can be appreciated from epistemic risk (see Epistemic Risk). Epistemic Degrees of Freedom—​a component of epistemic risk (see Epistemic Risk) that relates to the degrees of freedom necessary to make the conceptual linkage between hypotheses and predictions, as in the hypothetico-​ deductive approach (see Hypothetico-​Deductive Reasoning) or between the source and target domains used in the analogical approach (see Analogical Reasoning). The degrees of freedom relate to translations between linked arguments; for example, the number of instrumental assumptions and presuppositions. For example, hypotheses relating cognitive functions to neurometabolic image changes require a number of intervening translations, such as changes in deoxygenated hemoglobin to neuronal activities and then patterns of neuronal activities to cognitive functions.

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Epistemic Distance—​a component of epistemic risk (see Epistemic Risk) that relates to the conceptual distance between hypotheses and predictions, between the target and source domains of analogical reasoning employing the Fallacy of Pseudotransitivity, or to the use of syllogistic deduction, particularly as to how the bridging terms relate to the major and minor terms (e.g., creating syllogism where the bridging terms are potentially different when used in the major versus minor premise). Also see Epistemic Degrees of Freedom. Epistemic Risk—​a conceptual tool used for assessing the risks involved in the necessary use of fallacies. For example, epistemic risk can be used to judge hypotheses used in the scientific method or the hypothetico-​deductive approach to medical diagnosis. The hypothetico-​deductive approach (see Hypothetico-​Deductive Reasoning), which is the Fallacy of Confirming the Consequence, is of the form if a (the presence of disease A) implies b (a laboratory finding X) is true, b is true, then a is true. Epistemic risk, then, relates to the degree of reasonableness of making the connection between the presence of disease and the laboratory test used to support the diagnosis. Epistemic risk is composed of epistemic distance (see Epistemic Distance) and epistemic degrees of freedom (see Epistemic Degrees of Freedom). Evidence-​Based Medicine—​originally a discipline of systematic analysis of modes of justification and hence expectations for medical reasoning. Included as evidence were case reports, case series, professional opinions, and professional consensus in addition to clinical trials. While no hierarchy among the different modes was claimed initially, current evidence-​ based medicine has largely discounted all but randomized controlled trials, and many individuals and professional organizations hold evidence-​ based medicine synonymous with randomized controlled trials. However, these immediately risk loss of information according to the Second Law of Thermodynamics as Applied to Information (see Second Law of Thermodynamics as Applied to Information), which further risks the logical Fallacy of Four Terms (see Fallacy of Four Terms). Evolutionary Epistemology—​a generalization from evolutionary logic to include other methods of epistemology that are not necessarily logical in nature (see Evolutionary Logic). Whether there is much beyond logic is an open question, and conclusions should, as yet, be reserved. Evolutionary Logic—​a conceptualization that logic is a set of human-​evolved tools (epistemic tools) that reflect the structure of the universe (ontology) to which the tools are relevant. In other words, the universe is just that which required the evolution of epistemic tools, such as logic, to understand the universe. This extended form of logic (embracing the use of logical fallacies and probability in the form of partial syllogisms) is not analytical (i.e., true by definition or convention), although logic can be

Glossary of Concepts

defined and constructed in such a manner that suggests it is analytical. However, logic is a reflection of the structure of the relevant universe. Logic is synthetic exactly in the same manner as the synthetic a priori in Immanuel Kant’s Critique of Pure Reason (1781). When viewed in this manner, even violations or departures from logic principles in the traditional sense—​such as logical fallacies—​have great utility as epistemic tools. Consequently, this expanded notion of logic shows that logic is organic in that it arises from the needs of humans (and, arguably, all living things in one form or another). Extrapolation—​a method of inference that projects beyond the range of data from which it is based. For example, consider a set of measurements that follow from y = x, where x is a whole number ranging from 0 to 10. Next, one wants to know (extrapolate) the value of y when x is a whole number ranging from 11 to 15. Based on the assumption that the relation between y and x when x ranges between 0 and 10 also holds true, then y  =  11, 12, . . . 15, when x ranges between 11 and 15. However, there is no reason a priori that the relation y = x should remain for values of x ranging from 11 to 15. This is not to say that the relation y = x does not hold for values of x from 11 to 15; however, this can only be an assumption or presupposition. The situation is different in interpolation. Fallacy of Confirming the Consequence—​a logical error resulting from the misstatement of the modus ponens form of propositional logic (see Modus Ponens). The valid form is if a implies b is true and a is true, then b is true. The Fallacy of Confirming the Consequence is of the form if a implies b is true and b is true, then a is true. The problem with the fallacy is that b could be true of any number of reasons other than a. This fallacy is a consequence of the Inverse Problem (see Inverse Problem) and is the basis for the hypothetico-​deductive approach (see Hypothetico-​Deductive Reasoning) to medical decision-​making as well as the scientific method. Yet the judicious use of this fallacy is indispensable to gaining new knowledge, such as the patient’s specific diagnosis, or to new medical scientific knowledge, such as a new treatment. Because it is indispensable, the Fallacy of Confirming the Consequence is included in the discipline of logic, particularly evolutionary logic (see Evolutionary Logic). Fallacy of Four Terms—​a fallacy that is an extension of syllogistic deduction (see Syllogistic Deduction), which typically depends on three terms linked in two premises to form a conclusion. Each premise contains the bridging term with a major and minor term within the two premises. The validity depends on the bridging term being the same in both premises. If the bridging term is not the same, then the conclusion is invalid (not neither true nor false) and is called the Fallacy of Four Terms. The fallacy is indispensable in medical reasoning; indeed, it forms the basis for the use of randomized clinical trials. Consider the following syllogism:

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Major premise: Sample of patients with disease A responds to treatment X Minor premise: My patient has disease A. Conclusion: My patient will respond and needs treatment X. In this case, disease A is the bridging term. If my patient is not identical to the patients that make up the sample in every relevant manner, then there are two bridging terms, and thus the conclusion is invalid by the Fallacy of Four Terms. One bridging term is the sample of patients with disease A and the second is my patient. Fallacy of Induction—​the fallacy that holds that all inductions, such as every raven observed is black, therefore all ravens are black, are suspect, not that they are not true. The Fallacy of Induction holds that one cannot know that somewhere, unknown to the observer, a raven exists that is not black. This fallacy is different from the A  Priori Problem of Induction (see A Priori Problem of Induction). Fallacy of Limited Alternatives—​a fallacy that is an extension of the Fallacy of Confirming the Consequence in which there are multiple hypotheses and a single prediction and is of the form if (a or b or c) implies d is true; d is true, b and c are false, then a must be true. Regardless of the truth status of b or c, the conclusion that a is true is invalid because it represents the Fallacy of Confirming the Consequence. However, including b and c as hypotheses to be considered and then finding them false appears to lend confidence (falsely) that a is true. Furthermore, there may be some unknown other condition, e, that is responsible for d. This form of fallacy is particularly of concern when the route to a diagnosis is a process of elimination or diagnosis by exclusion. The risk for the Fallacy of Limited Alternatives depends on the nature of the “or” function, whether it is an “inclusive or” or an “exclusive or.” In an “exclusive or,” if one of a, b, or c is true, then the others must be false; and, if two are false, then the remaining must be true. In other words, finding c to be true means that b and a must be false. In an “inclusive or” function, any of the alternatives, a, b, or c, may be true, and at least one must be true. For example, confusing an exclusive or for an inclusive or function can lead to the Gambler’s Fallacy (see Gambler’s Fallacy). Fallacy of Pseudotransitivity—​a fallacy that is an extension of the Principle of Transitivity, the latter being of the form if a implies b and b implies c, then a implies c, which is valid. The invalid form, which makes up the Fallacy of Pseudotransitivity, is of the form if a implies b and c implies b, then a implies c. The fallacy is indispensable to medicine as it is the logical form of metaphors, which are critical (see Analogical Reasoning and Metaphor). The Fallacy of Pseudotransitivity is also fundamental to forming deductive syllogisms, where b becomes the bridging term that links the major

Glossary of Concepts

term, a, and the minor term, c, to form the conclusion that a implies c (see Syllogistic Deduction). Similarly, the Fallacy of Pseudotransitivity is fundamental to the Fallacy of Four Terms and its application to randomized controlled trials, for example (see Fallacy of Four Terms). Fuzzy Logic—​a type of logic that moderates the Principle of the Excluded Middle with the use of probability (see Probability). In this case, logical variables are not assigned a single value as true or false (in binary logic as 1’s or 0’s) but may have intermediate values, ranging, for example, from 0 to 1. Solving an argument then involves functors whose operations change the values, similar to athematic operations. Typically, the output is a dichotomous variable whose value is 1 or 0 and that has consequences for subsequent actions, such as deciding on a diagnosis or treatment. The difference between probability and fuzzy logic is that, typically, probability, as in a frequentist approach, is based on the expectation of a phenomenon in a particular context. The truth or falsehood of the expectation does not apply unless one makes a claim about the expectation that fails to materialize. In fuzzy logic, it is the degree of truth or falsehood that is of concern and is used to arrive at a conclusion. Gambler’s Fallacy—​originally, a fallacy in which past occurrences are thought to affect future occurrences of events that are independent. For example, a gambler might believe that his luck has been so poor previously that he is due for a win. Alternatively, a gambler on a winning streak believes that he will continue to win. In this book, the concept of the Gambler’s Fallacy is extended to diagnosis. For example, the prior presence of diabetes mellitus in a patient with a new diagnosis of peripheral neuropathy may hold that diabetes is the cause and other diagnoses in the differential diagnosis are not considered, which would be a mistake (see Fallacy of Limited Alternatives). Homeopathy—​a branch of the Empiric school of medicine that contrasts with allopathic medicine (see Allopathic Medicine). In homeopathy, the route to treatment lies in the concept of similia similibus curentur (like cures like). The operating principle was to administer the very agents thought to cause the illness in order to, at the very least, produce those signs and symptoms analogous to the patient’s symptoms to treat the illness. Initial doses are extremely low so as not to produce the illness, but then increase gradually. The symptoms and signs were analyzed to precise detail and were then related to a pharmacopeia of agents that produced some combination of the symptoms and signs. “Hoof Beats and Zebras” (Bayes’ Theorem)—​related to an admonition often raised as an example of clinical or practical reasoning. The full expression is “when you hear hoof beats, think of horses, not zebras (unless you are in Africa).” This and other clinical saws are cited as evidence that, many times, medical reasoning is not scientific or logical. This and other methods

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are cited as evidence for the necessity of extra-​logical reasoning. While rarely realized, this common clinical saw reflects Bayes’ theorem when applied to medical decision-​making. Not only does the clinician have to consider the sensitivity and specificity of a decision tool, such as a laboratory test or physical examination, but the clinician must also consider the like­ lihood that the patient has the condition or requires the treatment being considered. Hypothetico-​Deductive Reasoning—​arguably one of the most prevalent forms of medical reasoning (perhaps after pattern recognition) in which a hypothesis is developed (such as a diagnosis or most optimal therapy), which then is predictive of an event (such as the result of a diagnostic test or response to a treatment). The subsequent demonstration of the predictive event is taken as evidence for the truth of the hypothesis. The hypothetico-​deductive approach is an example of abduction, which, in reality, is the Fallacy of Confirming the Consequence (see Fallacy of Confirming the Consequence). An alternative often considered is pattern recognition (see Pattern Recognition), and, indeed, this method of reasoning became fashionable with the advent of problem-​or case-​based learning in medicine. The modus operandi was to have students mimic the actions of experts, which were viewed as pattern recognition. This attribution of pattern recognition to experts may be a fallacy based on the actor/​action distinction (see Actor/​Action Distinction). Pattern recognition is problematic because of the A Priori Problem of Induction (see A Priori Problem of Induction). Induction—​a form of logic in which commonalities among a set of observations are used to infer a general principle; for example, every raven observed is black, therefore all ravens are black. Induction is thought to be fundamental to science in general and to medical science in particular. The generalization is evidenced, or follows directly from the observations, and thus has the appeal as something self-​evident. However, induction is subject to the Fallacy of Induction (see Fallacy of Induction) and the A Priori Problem of Induction (see A Priori Problem of Induction). Induction, the A Priori Problem of—​see A Priori Problem of Induction. Information—​in this book, information will be considered in its widest connotation, that being nonrandom state changes. For example, a sensible English sentence can be considered a sequence of states corresponding to places in which letters, punctuations, and blanks are inserted. The order in which these are placed conveys information. Moving in sequence across the series of spaces produces nonrandom changes in the space viewed. This definition of information can be applied to the binary code of digital computers or to the sequence of base pairs in DNA. Thus defined, information can be considered inherent in any system that displays nonrandom behavior. Human health and disease are nonrandom; hence human health

Glossary of Concepts

and disease can be considered as information systems. Concepts related to information, discovery, and use are applicable to medical decision-​making. Informational Synonymy—​see Principle of Informational Synonymy. Interpolation—​a method of inference that “fills in the gap” between known observations. For example, consider a set of measurements that follow from where y = 0, 2, 4, 6, 8 . . . 10, when x = 0, 2, 4, 6, 8 . . . 10, respectively. The relationship between y and x can be given as y = x. Next, one wants to know (interpolate) the value of y when x is 1, 3, 5, 7 . . . 9. Based on the assumption that the relationship between y and x when x is 1, 3, 5, 7 . . . 9 is the same as when x = 2, 4, 6, 8 . . . 10, then y = 1, 3, 5, 7 . . . 9 when x is 1, 3, 5, 7 . . . 9, respectively. In this case, the values of x in the inference are “in between” the values of x in the observations. The values of y for x = 1, 3, 5, 7 . . . 9 are bounded by the values where x = 0, 2, 4, 6, 8 . . . 10. Unless there is some discontinuity in the function that describes the relationship between y and x between different values of x, there are relatively stronger grounds to believe that y = 1, 3, 5, 7 . . . 9 when x is 1, 3, 5, 7 . . . 9, respectively. This situation is different from extrapolation, where at least one direction of the values is unbounded. Inverse Problem—​this principle holds that if there are multiple causes of the same phenomenon, one cannot determine which cause is involved in the particular instance. For example, there are multiple causes of muscle weakness; thus, weakness alone does not indicate which cause is operative in the particular patient’s weakness. The situation is related to the Fallacy of Limited Alternatives (see Fallacy of Limited Alternatives) and the Duhem–​ Quine thesis (see Duhem–​Quine Thesis). Application of Bayes’ theorem to each of the potential causes can help focus attention on a more likely cause among the alternatives; however, this remains indeterminate. Joint Method of Agreement and Difference—​one of several methods used for attempting to extract causation from correlation in induction (Mill, 1843). For example, consider the situation where three phenomena, a, b, and c, are seen whenever three different consequences, x, y, and z, are found. Next, the situation is altered so that only phenomena b and c are observed, and it just so happens that only consequences y and z occur. From the Method of Difference, phenomenon a is thought related (likely in a causal way) to consequence x. Next, consider the situation where three phenomena, a, b, and c, are seen whenever three different consequences, x, y, and z, are found. Also, in a different circumstance, three phenomena are found, a, d, and e, associated with consequences x, w, and t. From the Method of Difference, phenomenon a is thought related (perhaps causally) to consequence x. Mereological Fallacy—​this is the fallacy of attributing to the part the function of the whole and was attributed to Aristotle. In medicine, an example would be to infer that the cerebellum controls the trajectory of movement. This is inferred from the observation that a patient demonstrates ataxic

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movements consequent to removal of the cerebellum. Yet it is likely that many other parts of the brain are involved in generating normal movement trajectories. This is evidenced by the fact that ataxic movements can arise from lesions of the frontal, parietal, and occipital lobes as well as from the peripheral sensory nerves. Modus Ponens—​a form of deduction that results in a true conclusion if the premises are true. An example would be if a implies b is true and a is true, then b is true. It does not matter what a or b actually are. The advantage is that any claim that can be shown to be in the form of modus ponens will result in true conclusions if the premises are true. Modus Tollens—​ a form of deduction where a definitive false conclusion demonstrates that at least one premises or proposition must be false or invalid, respectively. An example would be if a implies b is true and b is false, then a must be false. It does not matter what a or b actually are. The advantage is that any claim that can be shown to be in the form of modus tollens one can be certain that at least one premise is false or one proposition invalid. This is fundamental to the process of elimination that underlies some applications of the hypothetico-​deductive approach in medicine. Negative Predictive Value—​the probability that some decision method will demonstrate the negative to be true. For example, it could be the probability that if a patient has a negative test for a disease, the patient would not have the disease. It is important to note that a negative predictive value is not the same as the specificity, where the probability is that a person without the disease will have a negative test. The negative predictive value modifies the specificity by accounting for the probability that a person in the sample (group) of concern does not have the disease. Thus, a test with a high specificity still may not be useful if the disease is rare because there will be an increased risk of false positives. Partial Syllogism—​an example of a typical syllogistic deduction is all persons who smoke will develop cancer or heart disease; John Doe smokes; there­ fore, John Doe will die of cancer or heart disease. Note that the conclusion John Doe will die of cancer or heart disease has absolute certainty given the major premise that ALL persons who smoke will develop cancer or heart disease. Yet, there is reason to doubt the truth of the major premise—​ perhaps a smoker dies in an accident. The argument can be recast as a partial syllogism of the form some persons who smoke will develop cancer or heart disease; John Doe smokes; therefore, John Doe may die of cancer or heart disease. The partial syllogism does offer some knowledge, although it is uncertain. Pattern Recognition—​a reputed form of medical reasoning that is held to be different in kind to others such as the hypothetico-​deductive approach (see Hypothetico-​Deductive Reasoning). In pattern recognition, the symptoms and signs are matched to a single conclusion, such as a diagnosis, in a

Glossary of Concepts

many-​to-​one correspondence that appears to be intuitive. However, some prior decision must be made as to which symptoms and signs are to be used in pattern recognition, and thus pattern recognition is subject to the A Priori Problem of Induction (see A Priori Problem of Induction). Positive Predictive Value—​the probability that some decision method will demonstrate that the positive result is true. For example, it could be the probability that if a patient has a positive test for a disease, the patient will have the disease. It is important to note that a positive predictive value is not the same as sensitivity, which is the probability that a person with the disease will have a positive test. The positive predictive value modifies the sensitivity by accounting for the probability that a person in the sample (group) in question does have the disease. Thus, a test with high sensitivity may still not be useful if the disease is rare because there will be an increased risk of false negatives. Potentiality—​this term is used in the Aristotelian sense, where the contrasting term is actuality. For example, that an acorn will become a tree when it is not yet a tree suggests that there is the potentiality of a tree in the acorn. The critical question becomes, what is the ontological status of potentiality? Clearly, there must be something to the potentiality of a tree in the acorn as the acorn does not become a frog. The interesting notion is whether the potentiality of the tree is what causes the acorn to become a tree. In philosophical terms, the potentiality represents the telos or purpose. The purpose of the acorn is to become a tree. The concept of potentiality in this sense is important in notions of medicine, particularly neurological disorders, for example. Consider situations where any number of behaviors are possible, for example, one could reach for a cup in a great many different ways. Thus, each manner by which the cup could be grasped represents a potentiality. This potentiality has an ontological sense (a reality in some sense). Practical Judgment—​a description of medical reasoning that often, by its assertion, claims a distinction from scientific or logical reasoning. Often this description is applied when necessary judgments are reached in the absence of sufficient scientific information or when traditional forms of logic, such as deduction, are unsuccessful. However, when an expanded notion of logic is considered, specifically evolutionary logic, many of the operations of practical judgment can be seen as special cases in logic. The implication is that no fundamental epistemic differences exist between practical judgment and medical reasoning based on the extended forms of logic. Practical Syllogism—​syllogistic deduction (see Syllogistic Deductions) obtains the certainty of conclusions based on the Principle of the Excluded Middle (see Principle of the Excluded Middle) and the state-​of-​being linking verbs among major, minor, and bridging terms. The limitations of syllogistic deduction in gaining new knowledge, particularly those caused when

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requiring state-​of-​being verbs, were well known. Indeed, Aristotle offered a variation called the “practical syllogism,” where state-​of-​being verbs are replaced by verbs indicating different relations. Thus, practical syllogism provides much greater utility. But there is a cost because the linking verb “cause” must be explicated, which is a difficult notion. Principle of Causational Synonymy—​a concept dating back to the ancient Greeks that holds that, no matter the mechanisms in a causal agent that creates an effect, those mechanisms must be present in the effect. For example, consider my hand moving water. It is the electrons in the outer orbits of the atoms that make up the surface of my hand that repel the electrons in the outer orbit of the atoms that make up water. Thus, a complete explanation of why my hand can move water ultimately must include a discussion (whether implicit or explicit) of the electrons involved. Furthermore, the electrons involved in the cause (my hand) are of the same kind as in the effect (water). Complete explication of human behavior with causational synonymy is difficult and perhaps, in some cases, unobtainable. However, failure to reach complete causational synonymy is a source of misreasoning and error. Principle of Informational Synonymy—​a more general form of the Principle of Causational Synonymy (see Principle of Causational Synonymy), which holds that the information in the effect cannot be greater than the information in the cause (see Information). Greater information in the effect compared to the cause would be a violation of the Second Law of Thermodynamics as Applied to Information (see Second Law of Thermodynamics as Applied to Information). This is not to say that the information in the effect cannot be less than that in the cause. Many methods and approaches that enable medical decision-​making and scientific inferences result in a loss of information that makes the inferences uncertain and often limits utility. Principle of Parsimony—​also known as Occam’s razor. Occam said “it is vain to do with more what can be done with fewer” (attributed by Bertrand Russell, 1940). Generally, it holds that the simplest answer is most likely to be the true answer. While potentially useful in selecting which among competing hypotheses may first be submitted to vindication by experimentation, it is not vindication or verification itself. If competing hypotheses are independent—​that is, each has an independent probability of being true—​then the truth of the simplest hypothesis has no bearing on the truth or falsehood of alternative hypotheses, even if they are more complicated. Failure to realize the independence of the various alternatives can lead to the Fallacy of Limited Alternatives (see Fallacy of Limited Alternatives) and the Gambler’s Fallacy (see Gambler’s Fallacy). Principle of the Excluded Middle—​a fundamental principle of deductive logic and induction. It holds that a premise must be either true or false and cannot be both true and false or neither true nor false. It is this principle

Glossary of Concepts

that gives deductive logic its certainty and is derivative of the fact that logical propositions that link premises require state-​of-​being linking verbs. Probability—​relates to the chance of a specific event or circumstance being true (or false) or present (or absent). In this book, the argument is advanced that the primary value of probability is to reduce the constraints of the Principle of the Excluded Middle, thus allowing increased utility of logic now couched in probability terms; for example, the probability syllogism (see Probability Syllogism). Probability Syllogism—​a derivation from the partial syllogism (see Partial Syllogism) variant of deductive syllogism where the state-​of-​being linking verb is replaced by a conditional or qualitative verb such as may or some. However, the partial syllogism is imprecise and therefore highly uncertain. The relation can be replaced by a probability statement such as 80% of a’s are caused by disease b, one’s patient (c) has symptom a, therefore one’s pa­ tient has an 80% chance of having disease b. This is an example of a probability syllogism. Process Metaphor—​metaphors, derivative of the Fallacy of Pseudotransitivity (see Fallacy of Pseudotransitivity), are indispensable in medical decision-​ making and in medical science. In the process metaphor, the source domain is a process that is never completed or realized. An example would be Galileo’s notion of inertia, wherein an object in motion will always (infinitely over time) remain in motion unless acted upon by an outside source. Experiments demonstrated that a ball with the same momentum rolling up an inclined plane will rise to the same height. If the height of the incline is zero, then the ball would roll forever. In medicine and science, reduction often implies a process metaphor. Similarly, the Large Number Theorem in statistics is an example of a process metaphor. Quasi-​Fact—​typically a hypothesis (inference) that has the epistemic status of fact, often when used to argue for or against a theory. Many times, these hypotheses are considered self-​evident or never suspected as being other than fact, particularly when operating within a single or dominant theory. Randomized Controlled Trial—​also referred to by the abbreviation RCT; it represents an experimental design. The notion of “control” refers to attempts to mitigate the effects of confounds; for example, through the use of the placebo (or nocebo) effect. Randomization is another way to try to control the effects of confounds by allocating subjects prone to confounds to both the experimental and the control groups. Thus, RCTs are a powerful tool even though there are important limitations, recognition of which should help in the judicious use of RCTs (and ignorance of which can lead to injudicious use and error). A particular difficulty of RCTs in medical reasoning relative to the care of individual patients is that the RCT becomes the Fallacy of Four Terms when the inferences from a sample

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(subjects in a RCT involving disease A) are applied to the population of concern (all those with disease A). Reconstruction and Reductionism—​in the sense used in this book, reconstruction is taken as the complement of reductionism. Indeed, the value of reductionism—​that is, breaking down a phenomenon into an increasing number of entities of finer resolution—​is only valuable if the entities can be reconstructed back into the phenomenon or at least into other comparable phenomena. However, such reconstruction requires that each step in the reduction is fully reversible; specifically, that information is not lost according to the Second Law of Thermodynamics as Applied to Information (see Second Law of Thermodynamics as Applied to Information). The degree to which the original phenomenon or comparable phenomena cannot be reconstructed indicates the degree of information lost; for example, taking the mean (central tendency) of the improvement of a group of patients with disease A. Scientific Method—​ a procedure intended to generate new knowledge. As typically constructed, the following steps are (1)  identify or formulate a problem, the answer to which generates new knowledge; (2)  review current relevant information; (3) formulate a hypothesis that likely could be an answer; (4) convert the hypothesis to a testable hypothesis by deriving expectations that can be subjected to testing; (5) demonstrate whether the expectations result; and (6)  if the expectations do not result, reevaluate and reformulate the hypothesis. To this point, the scientific method is valid because it represents the logical deductive form of modes tollens (see Modus Tollens). However, this is not the manner in which the scientific method is used; that is, to purposefully demonstrate the expectations from the hypothesis will not be demonstrated. The vast majority of times, the experiments are designed to demonstrate the hypothesis, in which case the scientific method becomes abduction or better described as the Fallacy of Confirming the Consequence (see Fallacy of Confirming the Consequence). Second Law of Thermodynamics as Applied to Information—​the Second Law of Thermodynamics states that entropy cannot decrease in any closed system. Entropy is a measure of randomness or disorder and can be considered inversely proportional to the information (structure) contained in the system. Substituting information as the inverse of entropy into the Second Law of Thermodynamics means that, in any closed system, information cannot increase, only decrease. Furthermore, any process within the closed system that is irreversible will result in a loss of information (for an example, see Reconstruction and Reductionism). Solipsism—​originally a philosophical position in which all that is real is what an individual holds as real. There is an epistemic and an ontological sense. As an epistemic example, F. H. Bradley (1893) wrote “I cannot transcend experience, and experience must be my experience. From this it follows that

Glossary of Concepts

nothing beyond my self exists; for what is experience is its states.” The notion of solipsism used in this book is extended to the situation in which a person or discipline claims validation by means internal only to the person or discipline. In the case of a discipline, being internal can be understood as constraining evidence only to those notions inherent in the discipline. Thus, reasoning becomes self-​referential or circular and not subject to external vindication or refutation. For example, one form of evidence-​based medicine is that only the results of randomized controlled trials are legitimate evidence, and, to the extent that this is maintained, medicine becomes closed to alternatives. Syllogistic Deduction—​a form of logical deduction, in one example, consisting of major and minor premises and a conclusion. The premises and conclusions contain major, minor, and bridging terms. The general structure is: Bridging term is major term. Minor term is bridging term. CONCLUSION: Minor term is major term. MAJOR PREMISE: MINOR PREMISE:

An archetypical example would be: MAJOR PREMISE: All

humans are mortal. is human. CONCLUSION: Socrates is mortal. MINOR PREMISE: Socrates

Note that the verbs linking the terms are state-​of-​being verbs, are and is, which imply a status, such as membership. Thus, this form of syllogistic deduction can be understood from set theory, in which the major term (things mortal) is the universal set. The bridging term (human) is a subset wholly contained within the universal set (things mortal). The minor term (Socrates) is a subset wholly contained within the set of the bridging term. Consequently, the minor term (Socrates) must be a subset wholly contained within the universal set (things mortal). Tautology—​a situation in which a claim is true by definition or by virtue of being identical. By definition, the claim All unmarried men are bachelors is true by definition. A bachelor is an unmarried man. No new knowledge is gained. Similarly, when two diseases thought to be different are discovered to be the same, this is tautology: for example, the claim a = b is made and later b is discovered to just be a, then the claim a = b is true because a = a (Identity principle). Other than recognizing that the two diseases are the same, little new knowledge is gained. Type I Error—​generally associated with the statistical claim that two entities are statistically significantly different when in reality they are not different. The term can be generalized to any claim of difference when, in reality, they are not different.

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Type II Error—​generally associated with the statistical claim that two entities are not statistically significantly different when in reality they are different. The term can be generalized to any claim of no difference when in reality they are different.

COMPANION WEBSITE Appendix A, “A Very Brief and Selective Introduction to Logic” and Appendix B, “A Basic and Selective Introduction to Probability and Statistics” are available on this book’s companion website at www.oup.com/us/medicalreasoning.

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1

Introduction PLACES TO EXPLORE THE RAMIFICATIONS OF UNCERTAINTY

Certainty is perhaps the most important thing in medical care and is what clinicians struggle most to provide, as will be introduced in Chapter 2. The need for certainty supervenes on every interaction between clinicians and patients whether mediated by practical or clinical reasoning, scientific reasoning, or the application of science. The notion of certainty is the scalpel used to first cut any medical knowledge at its joints, as Socrates said, “That of dividing things again by classes, where the natural joints are, and not trying to break any part, after the manner of a bad carver” (Plato, Phaedrus 265d–​266a). Where and how to cut medical knowledge such that it may be analyzed and understood is suggested by the medical knowledge itself, just as a good carver looks for the joints in a leg that imply natural separations where cutting would be easiest. In fact, the place easiest to cut is the indicator of a natural separation. The bad carver cuts arbitrarily, in the process breaking apart into a distortion the medical knowledge the he sought understand. A diagnosis, evaluation program, and treatment recommendations are only as good as the certainty that clinicians and patients can have in them. Allegiance to and rejection of medicine throughout history often turn on the precariousness of certainty. Molière’s 1666 play “Le Médecin malgré lui” (“The Doctor in Spite of Himself ”), in which a pretend physician conveys great certainty despite being a charlatan, is an example of anti-​physician sentiment because of pretentious certainty. But it does emphasize the importance of certainty. The reported overuse of tests likely is not just a response to medical liability threats but also evidences the discomfort with uncertainty felt by clinicians and the refuge sought in “objective” tests. “Objectifying” certainty is one element in the debate between generalists and specialist physicians, as discussed in Chapter  5. The pressing need for certainty is seen in the historical development of medicine (Chapter 6). Indeed, practitioners of the two opposing general schools of medicine, what are now termed rationalist/​allopathic

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versus alternative medicine, often referred to as empirics, provided many of the same treatments, perhaps varying in degree, until the early 1900s. As will be discussed, how these schools differed is in their approach to dispel uncertainty rooted in their explanatory theories. These battles also were played out in licensure laws since the 1600s as to who has the authority to claim certainty for their profession. Even the physicians’ code can be seen as an attempt to preserve at least the appearance of certainty in a united front. In the first code of ethics of the American Medical Association (AMA) in 1847 (https://www.ama-assn. org/sites/default/files/media-browser/public/ethics/1847code_0.pdf), physicians were admonished to keep private and privileged any disagreements or evidence of uncertainty among themselves. The continued reluctance of some clinicians to criticize other physicians speaks to the uncertainty the reviewing clinicians may have in their own position. At times, the need and the quest for certainty may lead to clinicians’ patronizing a false display of unreasonable certainty, perhaps to soothe their apprehension of their own uncertainty. Certainty is the central theme of this book. Certainty—​or the more pressing version, uncertainty—​will be explored in many aspects, such as its ontological (the nature of the universe) and epistemological (how one knows the nature of the universe) origins; its historical manifestations; and its practical, sociological, and political ramifications. I will endeavor to demonstrate how clinicians, impelled to act for the sake of their patients, have deep and perhaps unrealized connections to the fundamental means by which to gain certainty. Indeed, the battle lines were already drawn since the ancient Greeks as to the route to certainty, resulting in the Rationalist (modern allopathic), Dogmatist, and Empiric (homeopathy as an example) schools of medicine. And the questions of certainty have not diminished in the era of evidence-​based medicine; if anything, the issues are made more acute. Indeed, a crisis of uncertainty has struck medicine and biomedical research, exemplified in irreproducible research previously thought vouchsafed by the vaulted peer-​review system (Chapter 2).

Deduction and Its Derivatives At a fundamental level, uncertainty reflects the structure of the universe in which clinicians operate, a universe that is inherently uncertain—​perhaps not unlike the Heisenberg uncertainty principle of quantum physics, Gödel’s incompleteness of mathematics, or the unpredictability of complex and chaotic systems. The laws of physics are the same for the weather as they are for driving a car. While the nature of the operations of a car is more certain, the nature of the weather is such that a prediction is uncertain. Whether the operations of the human body come to be seen more machine-​like through advances in biomedical research or remain weather-​like is uncertain.

Introduction

Understanding the universe in which clinicians operate is both a question of what is the nature of that universe (its ontology) and the means to know that nature (its epistemology). Whatever the ontology of health and disease, there is the presumption that it is regular, and thereby predictable, allowing diagnosis, prognosis, and treatment. It is not irrational, at least in its intent, which means that there is a logic to it. Whatever the ontology, it encompasses a logic that, if recognized, can be exploited. It is that logic, in its widest connotation, that is the primary focus in this text. To be sure, there are a great many factors that influence medical practice, including psychological, social, ethical, political, financial, and emotional. These are not specifically addressed in this book. These factors have been explored in detail in other excellent works that will be cited as the occasion arises. But the choice not to address them in order to more fully explicate the role of “logic” is not to denigrate the importance of these other factors and also should not be seen as a weakness in this text by those expert in these other factors. The choice to focus on logic means that the current effort should not be considered and judged as an exhaustive study of medical knowledge as it is reflected in practice and the relation of practice to biomedical science. Because certainty is the central issue, one can proceed from the perspective of asking, “What form of knowledge claim has the greatest certainty, regardless of the specific circumstances?” Clearly, since the ancient Greeks, the greatest confidence of any claim to knowledge is from deduction. Two types of deduction will be examined in the context of biomedical research and medical practice. One form is propositional logic, which is of the form if a implies b is true and a is true then b is true. Indeed, b cannot be anything else but true. In this form, premise a is the antecedent, premise b is the consequence, and proposition a implies b the implication. Thus, a claim to knowledge in this form, referred to as modus ponens, has absolute certainty (see Chapter 5 and appendix A, available at www.oup.com/us/medicalreasoning). In a sense, test result A IS disease B and represents tautology. The implies (implication) is analogous to a state-​of-​being  verb. The modus ponens form of propositional logic does not create new knowl­ edge because it is a tautology. The question becomes then how to gain new knowledge to aid medical reasoning yet still borrow from the certainty provided by modus ponens deductive logic. The logical structure of most attempts to gain new knowledge is in the form if disease B implies test result A and test result A is found, then disease B must be true. The physician or healthcare professional makes a prediction based on a suspicion that the patient has disease B. The test is ordered, the patient is found to have test result A, and a diagnosis is made. This is the hypothetico-​deductive approach to diagnosis. However, this form of argument is the Fallacy of Confirming the Consequence and is invalid. This is not to say that, having test result A, the patient cannot have disease B, but only that the physician or healthcare professional cannot be certain that the

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patient has disease B, and certainty is what it is all about. Yet the Fallacy of Confirming the Consequence appears similar to the modus ponens version that provides absolute certainty. This form will be referred to as the modus ponens-​ like version of deduction at the root of the hypothetico-​deductive approach in medical reasoning. Ways to mitigate this uncertainty are discussed in Chapter 3. The same fallacy holds when the physician or healthcare professional makes a prediction that the patient will benefit from treatment. In this case, the argument is treatment C is effective (implies the use) for disease D, the patient has disease D, therefore the patient should receive treatment C. There is another possibility of the argument form if a implies b is true (hypothesis) and b is false (prediction) then a is false. This is a valid argument termed modus tollens and is the basis for ruling out candidate diagnoses. The logical Fallacy of Confirming the Consequence, of the form if a implies b is true (hypothesis) and b is true (prediction) then a is true, is the basis of the scientific method by which biomedical research gains new knowledge. When used in a modus ponens-​like manner (note that it is not a true modus ponens), the scientific method is a logical fallacy. It is only the modus tollens form of the scientific method deliberately conducted to disprove a hypothesis that is certain. In science, one virtually never sees experiments constructed as modus tollens to prove the prediction wrong, even though, if successful, the demonstration would be absolutely certain. Similarly, most clinicians very frequently do not use the modus tollens form of an argument to rule out a diagnosis because most decisions are ruled in and thus subject to the risk of uncertainty entailed by the Fallacy of Confirming the Consequence. Again, this is not to say that the diagnosis ruled in cannot be true, but only that it cannot be certain. Given the vast majority of biomedical research and many ruled-​in diagnoses, steps can be taken to reduce the risks of an injudicious use of the Fallacy of Confirming the Consequence (Chapter 5). Another form of certainty via deduction is the syllogistic deduction, which is of the type described in Argument 1.1. Like the propositional logic discussed earlier, a true syllogistic deduction is a tautology and provides no new knowledge (Chapter 5). However, a variation of syllogistic deduction, the Fallacy of Four Terms, can be used to gain new knowledge. Consider the following syllogism: Argument 1.1 Major premise: All humans (bridging term) are mortal (major term). Minor premise: Socrates (minor term) is a human (bridging term). Conclusion: Socrates is mortal. What if Socrates is a different kind of human, one who will not die? Then there are two forms of the bridging term, human, of the major premise all of whom will die, and the bridging term, human, in the minor premise, which is of the kind of whom will not die. There are four terms: the major and minor terms and two kinds of bridging terms. In this case, one cannot conclude validly that Socrates is mortal; hence, the Fallacy of Four Terms.

Introduction

Virtually every experiment in biomedical research is the Fallacy of Four Terms, strictly speaking, and can give rise to false conclusions that cause no end of mischief but also provide new knowledge. Consider the following syllogism: Argument 1.2 Major premise: Humans with disease A in sample B (bridging term) respond to treatment C (major term). Minor premise: My patient (minor term) has disease A (bridging term). Conclusion: My patient should receive treatment C. But what if humans with disease A in sample B are all young and Caucasian without comorbidities and my patient is an elderly African American and frail? Then, my patient is not the same disease A as humans with disease A in sample B. Thus, there is the risk of the Fallacy of Four Terms, which means that one cannot be certain that my patient will respond to treatment C. The risk of the Fallacy of Four Terms is ubiquitous in biomedical research, but it can be a source of new knowledge—​consider Argument 1.3. Argument 1.3 Major premise: Rodents with disease A (bridging term) respond to treatment C (major term). Minor premise: Human patient (minor term) has disease A (bridging term). Conclusion: Human patients may respond to treatment C. The syllogism does not provide any evidence that human patients may respond to treatment C because there actually may be two versions of the bridging term and thus the Fallacy of Four Terms. It just may be that disease A in rodents is different from disease A in humans. Nonetheless, the syllogism reasonably suggests a clinical trial of treatment C for humans with disease A, and, the more similar disease A in rodents is to disease A in humans, the more likely the clinical trial will be productive. Means to mitigate the risks are discussed in greater detail in Chapter 5. The certainty of propositional and syllogistic deduction comes from the relationship between antecedent and consequence through the implication and between the major and minor terms through the bridging term. All of the relationships are state-​of-​being verbs (Chapter  5 and appendix A, available at www.oup.com/us/medicalreasoning). Such relationships require the Principle of the Excluded Middle. This principle holds that a premise must be either true or false, not neither true nor false and not both true and false. But seldom in medical practice or biomedical research are the relationships between hypotheses and predictions all-​or-​nothing propositions, as would be required by the Principle of the Excluded Middle. Thus, certainty is obtained at the expense of utility. Philosophical logic has devised alternative logical systems to loosen the hold of the Principle of the Excluded Middle (Burgess, 2009). For example,

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first-​and second-​order predicate logic avoids the all-​or-​nothing conundrum through the use of existential quantifiers (for example, There exists some a such that a relation to b, written as∃a : a → b) and the universal quantifier (For all a there exists a in relation to b, written as∀a : a → b ). In the existential qualifier, there exists some a (∃a), meaning that if there is only one true instance of a, then the statement there exists some a that is completely true and not false and thus has the same effect of the Principle of the Excluded Middle. It does so because the argument only requires a single instance of a (rather than all instances of a) to be true. The universal quantifier clearly conforms to the Principle of the Excluded Middle. Modal logic utilizes relations such as may be, is, or must be. Another example is fuzzy logic. Considerable work is being applied to developing alternative logical systems, such as those described previously, to aid in medical reasoning (Sadegh-​Zadeh, 2012). However, most, if not all, give up certainty, principally by giving up the Principle of the Excluded Middle, in exchange for greater utility. The logical alternatives are not the subject matter of this text because variations on propositional and syllogistic deduction are sufficient for the purposes of this effort. As will be demonstrated later, just an appreciation of propositional and syllogistic deduction and its variants explains a great deal about the nature and use of medical knowledge. These partial syllogism variants will be demonstrated to yield probability and statistics, and practical syllogism yields notions of cause and effect by which to generate hypotheses for diagnosis, treatment, prognosis, and biomedical research.

Probability and Statistics The certainty of propositional and syllogistic detection is obtained through the Principle of the Excluded Middle. But the universe of medical practice and biomedical science is not like that. Consider the following syllogism: Argument 1.4 Major premise:  Humans who smoke tobacco (bridging term) will die of cancer or heart disease (major term). Minor premise: Jack (minor term) smokes tobacco (bridging term). Conclusion: Jack will die of cancer or heart disease. Jack necessarily will die but not necessarily from cancer or heart disease. In medical practice, the syllogism is rephrased to the partial syllogism that follows: Argument 1.5 Major premise: Some humans who smoke tobacco (bridging term) will die of cancer or heart disease (major term).

Introduction

Minor premise: Jack (minor term) smokes tobacco (bridging term). Conclusion: Jack may die of cancer or heart disease. This partial syllogism is more reflective of reality and therefore has greater utility, but at the cost of certainty. One cannot know whether Jack will or will not die of cancer or heart disease. One could increase the utility of the partial syllogism by rephrasing as described here: Argument 1.6 Major premise:  Eighty percent of humans who smoke tobacco (bridging term) will die of cancer or heart disease (major term). Minor premise: Jack (minor term) smokes tobacco A (bridging term). Conclusion: Jack has an 80% chance of dying of cancer or heart disease. The certainty of the syllogism is improved by the introduction of probability (estimation of chance), but still one cannot know whether Jack is going to be among the 80% who will die of cancer or heart disease or among the 20% who will not. Syllogisms of the sort in Argument 1.6 will be referred to as proba­ bility syllogisms. Also, it is important to note that the syllogism does not gain validity by introduction of the probability, as will be clarified later. The validity of the probability syllogism still comes from the logical structure of the syllogism. Thus, in any situations where the use of probability syllogisms is used implicitly or explicitly and the results are in an error, it is likely that the error was due to an injudicious use of a logically fallacious probability syllogism. This is discussed in greater detail in Chapter 5 and in Appendix B (available at www.oup.com/us/medicalreasoning). In the probability syllogism described in Argument 1.6, where did the 80% come from? One can only have confidence to the degree that one has confidence in the probability premise. Perhaps the figure of 80% was obtained from a review of 10 death certificates in persons known to have smoked. But if it was obtained from a review of 1,000,000 death certificates, then one could have greater confidence in the probability premise. Thus, one has to introduce statistics as a means to gain confidence in the probability syllogism, which was in response to the uncertainty of the partial syllogism. But what is the nature of the statistics? One could say that the mean probability is 80% and the standard deviation is 10%. This results in a 95% confidence interval of 60.4–​99.6%, which means that the real probability has a 95% chance of being somewhere between 60.4% and 99.6%. Note that the chance of the real mean probability being 60.4% is the same as the chance that the real mean probability is 99.6%. The consequences of a real mean of 60.4% versus 99.6% could be large, perhaps catastrophic. The peculiar nature of the mean is discussed in Chapter 8. Importantly, the increase in utility of the probability syllogism creates an outcome that is a continuous variable, with a potentially infinite number of values between 0 and 1.  The probability is not a dichotomous medical

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decision, for example, “yes” or “no” or “true” or “false.” The range of possible values of a probability is much more like the human experience of entities and conditions; for example, a range of normal blood glucose levels. The human experience is only rarely just “yes” or “no” or “true” or “false.” The great paradox is that when attempting to escape the Principle of the Excluded Middle using the probability syllogism, the clinician finds herself having to fall back to the Principle of the Excluded Middle. One does not provide 80% of a treatment because the possibility of the disease is 80%. The decision is either not treat (0% of treatment) or completely treat (100% of the treatment). Thus, the conundrum is to determine the probability of a disease (for example, 80%) such that any probability greater than or equal to 80% results in 100% treatment and anything less than 80% results in 0% treatment. Typically, thresholding is required in the form of some cutoff in the value of the continuous variable—​the probability of the disease—​sufficient to warrant some action on the part of the clinician. Often the threshold cannot be determined based on the biomedical research that determined the probabilities. Establishing the decision-​making threshold in medical practice is strictly determined by the full consequences of the decision. The consequences are many and practical but can be summarized, at least partially, on the dimensions of benefit (the ethical principle of benefice) and the obligation to provide it, harm (the ethical principle of nonmaleficence), and respect for those upon whom the consequences fall (the ethical principle of respect for autonomy), such as for the individual and society. These dimensions construct a logic of ethics. It seems counterintuitive that a logic of ethics can be constructed in the sense of logical operators on ethical premises. The strangeness suggests a reticence in fully embracing logic, particularly in the evolutionary form of logic proposed. The ethical principles also include the principle of justice, which forms the relationships between premises that determine the consequences of decisions made by the clinician and the patient. The ethical principles provide the logical form of an argument with a particular circumstance, a particular patient, and a particular clinician providing the premises. Examples are described in detail in Chapter 3. The necessity of the partial syllogism is to mitigate the Principle of the Excluded Middle, which itself is a consequence of the state-​ of-​ being relationships between premises and contained in propositions. In a state-​ of-​being relationship, typically the entity is or is not, and not something in between. The partial syllogism modifies this sense of the state-​of-​being relationship to one of partial identity. However, there are many other forms of linkage typically derived from an action verb; in medicine, cause is a critical action verb describing a type of logical relation. Consequently, dating back to Aristotle (384–​322 bc), philosophers/​logicians have offered the practical syllogism, such as the one described in Argument 1.7.

Introduction

Argument 1.7 Major premise:  Smoking tobacco (bridging term) causes cancer or heart disease (major term). Minor premise: Jack (minor term) smokes tobacco (bridging term). Conclusion: Jack should stop smoking tobacco. Note that the syllogism given is not a valid syllogistic deduction because the relationship between the bridging term and the major term in the major premise is not a state-​of-​being linking verb. Consequently, the conclusion is not a direct consequence, as it might be if the syllogism was rephrased as the valid syllogistic deduction in Argument 1.4. Practical syllogisms, such as those that invoke causation, now referred to as causal syllogisms, are critical in medical practice as they often serve when an explicit probability syllogism is not available. One of the contributions of evidence-​based medicine, particularly randomized controlled trials, is the ability to convert many medical practice questions to an explicit probability syllogism. However, only a very tiny fraction of medical decisions can refer directly to randomized controlled trials, and one must look elsewhere to increase the certainty in the probability. Even if a particular medical decision has been studied in a randomized controlled trial, the Fallacy of Four Terms limits the applicability of the trial results to the treatment of the individual patient, as exemplified in Arguments 1.5 and 1.6. Again, one must look elsewhere for means of translating the probabilities from the sample studied to the individual patient whose decision about treatment is pending. Probability syllogisms are incomplete in the logical sense. Church’s theorem holds that, in such formal systems, there is no foolproof way to know the truth or falsehood of the dichotomous outcome (for example, whether the patient has or does not have disease A). In nearly every situation, rationalist/​allopathic physicians (most physicians today), unlike empiricists (of which homeopathy is an example), look to the notion of causation to inform the probability syllogism (Chapter 6). If the notion of causation is strong—​for example, in the case of tobacco smoking and cancer and heart disease—​the clinician is more inclined to see the real probability more toward the confidence interval of higher probability and therefore press harder for Jack to stop smoking. The causal or practical syllogism is central and critical to medical practice and biomedical research. In the case of rationalist/​allopathic medicine, notions of causation are fundamental to the hypothetico-​deductive approach to diagnosis and treatment decisions. In biomedical research, causation generates the hypotheses that are subject to the scientific method. The large majority of hypotheses are generated through the logical Fallacy of Pseudotransitivity, otherwise known as metaphor and discussed in more detail in Chapters 5 and 10. Briefly, metaphors have a target domain that gets its credibility from the source domain; for example, aspirin appears to help with pain from arthritis

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Medical Reasoning

so perhaps it will help with pain from a tooth problem. The credibility of the intention to use aspirin for a tooth problem based on its efficacy in arthritis pain depends on the claim that the causal mechanisms involved in tooth pain are similar enough to those involved in arthritis pain. Indeed, this is the basis for the “off-​label” use of approved drugs, biologics, and devices approved by agencies such as the US Food and Drug Administration (FDA). These issues are discussed in detail in Chapters 5 and 10. The causal syllogism is only as strong as the notion of cause and, particularly, how cause acts as the bridging term in the deduction. However, the notion of cause is very complicated and fraught with misunderstandings, misuse, and abuse. Methods are available to assess the credibility of the specific notion of cause and thus the certainty that attends the conclusion of the causal syllogism. One method, available from ancient Greek philosophers, is the Principle of Causational Synonymy—​whatever is in the cause that generates an effect must also be in the effect that responds to the cause. This principle is extended to the Principle of Informational Synonymy. These principles and their ramifications in medical practice and biomedical research are addressed in detail in Chapter 9. In a real sense, statistics is applying probability to probabilities that are applied to the partial syllogism, which is applied to syllogistic deduction. The causal syllogism is applied to the probability syllogism, which is applied to the partial syllogism, which is applied to the syllogistic deduction. Thus, fundamentally, the practice of medicine ultimately rests on propositional and syllogistic deduction and its extensions to the use of fallacies and to partial and practical syllogisms, used both judiciously and injudiciously (Chapter  5). An understanding of logic can therefore benefit clinicians and biomedical scientists.

Extra-​Logical Considerations Often, medical decision-​making requires thresholding the continuous outcome variable of the probability syllogism. In biomedical research, thresholding relates to internal certainty based on the consequences of either the truth or falsehood of the hypothesis for the larger theory, paradigm, or approach being tested. This is very different in medical practice, where the consequences are external and relate to ethical and socioeconomic consequences (Chapter 15). Critically, although perhaps not obviously, these external considerations are central to every decision in medical practice. No discussion of the practice of medicine is complete without considerations of the ethics, such as the principle of respect for autonomy, which are deeply involved in the relationships between patient and clinician. Beyond the legalistic aspects of autonomy, there is the implication of the principle of respect for autonomy in the powerful relationship between patient and clinician. Asymmetry in the power relation between patient and physician clearly affects the information

Introduction

transfer between patients and their surrogates and the clinician and between different clinicians, which impacts the efficacy and cost of medical practice. The first code of ethics of the AMA in 1847 proscribed the role of patients, which included not calling upon the physician during the dinner hour (Chapter 6). In the past, these relationships were not necessarily exploitive, but were consistent with the philosophical (metaphysical) notions of the Enlightenment, where there is an external reality that is accessible by science and which can be affected in medical practice. Thus, biomedical science and medical practice operated from privileged information gained above the human fray. In very important ways, both as a benefit and as a challenge, postmodernism is challenging the presuppositions of the Enlightenment. Biomedical research and medical practice do not have privileged knowledge, and whatever knowledge had is as precarious as any other claim to human knowledge. Consequently, postmodernism now plays a more discussed role in medical practice—​noting that it is a role that always has been present in the subtext. The practice of medicine has never been a relationship just between the patient and the physician. Society, through the agency of government, has always been involved, as, for example, in the Poor Laws of England in the 1600s. Licensure laws afforded some level of certainty in practices, although often for political intents. The apothecarist/​surgeon’s (forerunner of current general practitioners in the Anglo tradition) practice was circumscribed by practice laws supervised by the physicians of those times. In the early 1800s, deprived of legal protections afforded by restrictive licensure, rationalist/​ allopathic physicians had to take other steps to demarcate and legitimize themselves and, consequently, delegitimize other competitors. This is seen in the 1847 Code of Ethics of the AMA and in the subsequent finding of illegal monopolistic practices of the AMA that forced a revision of that code (Chapter 6). Involvement of the state also influences the manner in which medical practice is conducted. It would be naïve to think that the interposition of for-​profit and governmental insurers has not influenced the care rendered, a naïvety that patients and their supporters need to awaken themselves from periodically. However, it is not a naïvety suffered by most clinicians. Modern block grants and capitative care reimbursements, taken as threatening by clinicians today, were operational in the 1700s in England (Chapter 6). Also interposed between the patient and the clinician is the clinicians’ sense of their concern for themselves competing with their concern—​and hence decision-​making—​with regards to the patient. The clinician’s self-​concerns are not just monetary or for independence. They also are psychological and moral. There is enormous pressure on the clinician to make a diagnosis, and, unfortunately, in difficult situations, a diagnosis of psychogenic causes becomes the deus et machina (Chapter 5). Often it seems that medicine operates extra-​logically, outside a formal logical system. This is not entirely accurate, as will be discussed later when

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intuition in medicine is considered (Chapter 19). Fortunately, there have been many excellent works devoted to these concerns, but space limitations prohibit any attempt of doing these concerns justice in this text. At appropriate points in this narrative, applicable works will be cited for the interested reader to pursue.

Induction The remarkable utility of propositional and syllogistic deduction and their extensions for clinicians and biomedical researchers lies in the generality of its forms. For example, every argument or statement of the forms if a implies b is true and a is true then b must be true (by modus ponens); if a implies b is true and b is false then a must be false (by modus tollens); and if all a’s are b’s, and c is an a, then c is a b (by syllogistic deduction and the Principle of Transitivity in propositional logic) is true regardless of what a, b, and c are in any particular circumstance. Similarly, every argument or statement of the form if a implies b is true and b is true then a must be true (Fallacy of Confirming the Consequence) and if a implies b is true and c implies b is true then a implies c is true (Fallacy of Pseudotransitivity) is invalid in the sense of not providing certainty, regardless of what a, b, and c are in any particular circumstance. The trick is for clinicians and scientists to recognize these general forms when they are presented as particulars. The power of such deductive logic was called “blind thought” by Gottfried Wilhelm (von) Leibniz (1646–​1716). The formalism of logic was thought to provide the program, schema, or scaffolding on which to construct all human knowledge, and this was the mission of the Logical Positivists and Logical Empiricists of the early 1900s. With the scaffolding in place, all that was necessary was to provide the particulars that would specify the antecedents and consequences of the propositions, and the conclusion, as new knowledge, would freely follow. All that was necessary was for the clinician and the scientist to provide the particulars to hang on the scaffolding. The schema of blind thought and its application by the Logical Positivists and Logical Empiricists faded out with the proofs of Gödel’s incompleteness theorem (i.e., there are truths [theorems] in any sufficiently complex logical system that cannot be proven), Alan Turing’s proof regarding the halting problem (there is no computer program that can determine whether another program will stop at the right answer in a finite period of time), and the Heisenberg uncertainty principle (one cannot know both the position and the momentum of a particle to an arbitrary degree of certainty) (Chapter 19). Despite these limitations, which are limitations at the extremes, logic is still a powerful tool when wielded properly by the clinician and scientist; this includes the judicious use of logical fallacies.

Introduction

Whether the premises a, b, or a implies b are true is an empiric statement. The truth of the premises is grounded in some specific experience, such as in a scientific or clinical observation. What a and b are and what a implies b means are not derivable from propositional or syllogistic deduction unless it is the realization that a and b are the same, as in a tautology. The question then becomes what are the sources of the specific a’s, b’s, and a implies b’s? One answer comes from the logic of induction, which is addressed in detail in Chapter 5. Briefly, induction can be appreciated in the following argument:  every raven seen has been black, therefore all ravens are black. The induction involves creating a general principle, all ravens are black, from a set of particulars, every raven seen. In medicine, the induction may be that every patient with a sore throat, exudate in the posterior pharynx, fever, and lymphadenopathy has strepto­ coccal pharyngitis. This induction allows the creation of a syllogistic deduction, as shown here: Argument 1.8 Major premise:  Every patient with a sore throat, exudate in the posterior pharynx, fever, and lymphadenopathy (bridging term) has streptococcal pharyngitis (major term). Minor premise: Jack (minor term) has a sore throat, exudate in the posterior pharynx, fever, and lymphadenopathy (bridging term). Conclusion: Jack has streptococcal pharyngitis. This is not to say that the generalization is true. However, the process of induction allows the creation of the generalizations to be a hypothesis that is subject to experimental vindications (verification and validation are not appropriate terms in these contexts). Thus, this form of induction, called induction by enumeration, creates the premise that becomes material for the blind thought schema of deductive logic and is critical to medical practice and biomedical research. Induction also allows for the generation of propositions, such as a implies b, that are used to create the logical scaffolding of clinical and scientific arguments. Induction by enumeration, as described earlier, generates what it is to be an a and what it is to be a b. Next, one can observe the nature or behavior of a relative to b. For example, if every time a is seen, b is also seen or never seen, the proposition can be constructed such that a implies b where, in one case, a may cause or result from b, or where a prevents b, or b prevents a in some manner, respectively. Cause or result is the implication. John Stuart Mill (1806–​1873) established a set of methods to guide induction to implications or relationships between premises arrived at by induction by enumeration. These are discussed in detail in Chapter 5, particularly Mill’s method of concomitant variations, which is the basis of the statistical method of correlation that is widely used in medical practice and biomedical research.

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Interestingly, induction can be used to generate a logic for ethical and legal principles, disciplines often not thought of as science or scientific. For example, a survey of reasonable persons is held to demonstrate a common morality that organizes into concepts of respect for autonomy, beneficence, nonmaleficence, and justice. These induced concepts then are held to be principles for logical operations in ethics (Beauchamp and Childress, 2013). Indeed, ethics, as in case law, looks for precedents to assist in ethical decision-​making, and such precedence is a form of induction. Another form of induction to ethics is what Beauchamp and Childress refer to as common morality (Beauchamp and Childress, 2013, p. 4), which is a type of universal morality based on induction from all reasonable persons. Common morality gives rise to a series of ethical standards, including beneficence, nonmaleficence, respect for autonomy, and justice, among others. To be sure, there are alternative schema to the notion of common morality where authority does not come from the individual acting as a collective. These include theological schemas where authority comes from other than humans, such as God or God’s proxy in religious leaders (including kings, by the idea of the divine right of kings). Or the schema can be secular, where individuals subordinate their rights to a sovereign who has absolute power, with very limited exceptions (Hobbes, 1651). The point here is that even something that appears not science or not an exercise in scientific reasoning, such as ethics, can be approached by a form of scientific reasoning that is induction. This issue will be revisited in greater detail in Chapter 19, where a sharp distinction between practical, clinical, and scientific reasoning and science will be critiqued. A number of conundrums limit the certainty of induction, and these, as well as methods to mitigate the effects, are addressed in Chapter 5. These problems include the Fallacy of Induction and the A Priori Problem of Induction. Just for introductory purposes here, the Fallacy of Induction is exemplified by the induction every raven seen has been black, therefore all ravens are black. The fallacy stems from the fact that the observer cannot be sure that somewhere, unknown to the observer, is a raven that is not black. Similarly, the observer who induced that every patient with a sore throat, exudate in the posterior pharynx, fever, and lymphadenopathy has streptococcal pharyngitis cannot be sure that somewhere there is a patient who has a sore throat, exudate in the posterior pharynx, fever, and lymphadenopathy but does not have streptococcal pharyngitis. The consequence is that the induction every patient with a sore throat, exudate in the posterior pharynx, fever, and lymphade­ nopathy has streptococcal pharyngitis, which is used as the bridging term in the major premise in Argument 1.8, is not the same as the group, perhaps of one, who has a sore throat, exudate in the posterior pharynx, fever, and lymphadenop­ athy, which is used as the bridging term in the minor premise. The patient may actually have a pharyngitis from some different organism. The consequence is

Introduction

the Fallacy of Four Terms. As will be seen, there are methods to mitigate the Fallacy of Induction, reviewed in Chapter 5. The A Priori Problem of Induction relates to the way in which observations are relevant to assembling the particulars in order to induce a general principle. For example, what is it about the birds that include ravens, but not others, that the induction that all ravens are black can be made? Note that it cannot include the fact that the birds are black. In the case of streptococcal pharyngitis, why is not height, weight, hair color, and other factors not included along with sore throat, exudate in the posterior pharynx, fever, and lymphadenopathy? Clearly, there is some prior notion about the relevant observations that direct the choice of which entities are collected prior to the induction being formed. A  poor choice can result in misleading inductions. These issues are addressed in detail in Chapter 5.

The Discipline of Logic A commitment to logic enables a discipline that helps establish a type of certainty that lends credibility to actions taken by clinicians and scientists. That certainty, which comes as a consistency of action in like circumstances, often entails subjugating personal whims and self-​service. In contrast, there is a philosophical perspective called solipsism, which, in its original form, held that the only things that can be thought true are those things that a person thinks of. In the weak form, solipsism is trivially true because all anyone knows for sure is what he or she is thinking. One only holds thoughts in one’s head; one does not hold cars, trains, boats, and planes in one’s head. A problem with solipsism arises if it is not somehow rooted in some reality that transcends the moment. Imagine the chaos if every possible thing that can and needs to be known must be learned anew each day or each moment. One requires the notion of object permanence—​that the world persists while one blinks one’s eyes. Failure of conceptual permanence results in the Solipsism of the Present Moment, where nothing can be known outside of immediate sensations. The ability to induce general principles is a form of conceptual per­ manence. The consistency and continuity in utilizing axioms and rules of inference in formal logical systems are types of conceptual permanence. Another problem with solipsism, when taken to its logical extreme, is that there is no way in which another person can be suaded because appeals to solipsism end the conversation. Surprisingly, some clinicians engage in solipsism. Some clinicians hold that their own personal experiences are sufficient to justify any action. Such solipsism is even more egregious when those clinicians insist that the true or correct action is within their experience when it is not. In the absence of evidence-​based medicine, particularly of the type that only admits randomized

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Medical Reasoning

control trials are legitimate, some clinicians engage in solipsism by nihilism or by adventurism. Even insisting that evidence-​based medicine is the sole arbiter of legitimate knowledge is a form of solipsism. Similarly, the insistence that FDA approval is the sole arbiter of how drugs, biologics, and devices are used, independent of the regulation of interstate commerce in drugs, biologics, and devices, is a form of solipsism. This form of solipsism extends to insurance providers who use FDA approval as the arbiter of treatments provided. These issues are tackled in Chapter 18. Ultimately, for any clinician and scientist, it is a matter of personal choice and commitment (Chapter 19). At least scientists undergo peer review, whether for grants, publications, or tenure. The constraints about clinicians center around being accepted into the profession, such as graduation from professional schools and then licensure. Subsequent constraints are paltry, at least when compared to the authority taken by physicians. Clearly, these issues factor into the practice of medicine and are discussed in Chapter 19.

Origins of Ideas as Hypotheses Any antidote to the Solipsism of the Present Moment is something that transcends the specifics at the present moment. Transcending requires some generalization over time and sensations. The world remains intact when one closes one’s eyes, it is not instantaneously destroyed upon closure and instantaneously created once the eyes are open. Even if one were to turn one’s head while one’s eyes are closed, such that the actual sensations upon opening the eyes will be different, the world still is held to be continuous. Hypotheses transcend the moment both in time and in sensation. Hypotheses are predictions and thus an expectation of what may happen in the future; for example, one will come to a diagnosis or one’s hypothesis regarding biological mechanisms will be vindicated. It transcends the sensations that gave rise to the hypothesis because the sensations that subsequently vindicate the hypothesis are different, but any connection between the two sets of sensations is thought to be continuous or subsequent to the same causes. Every second of every day, humans implicitly generate and operate on hypotheses, even if it is to decide whether to take an umbrella as they exit their homes. In medical practice, hypotheses are paramount (Chapter 10). The significance of hypotheses is explicitly evident in the hypothetico-​deductive approach to diagnosis, for example. Some have argued for the alternative of pattern recog­ nition as somehow distinct from the hypothetico-​deductive approach. However, on critical reflection, this cannot be the case. Those who engage in pattern recognition, which arguably is the most common approach, engage in implicit or

Introduction

surreptitious hypothesis generation simply by what facts or observations are gathered to form a pattern—​the A Priori Problem of Induction. Thus, those using pattern recognition are just as dependent on logic as any other clinician or scientist. In a fundamental sense, any medical practice or biomedical research is only as good as the hypotheses generated and tested. To be sure, induction is an important aspect of hypothesis generation, but, as described in the A Priori Problem of Induction, induction presupposes a metaphysics consisting of prior knowledge projected forward through logic. Given the investments in medical practice and biomedical science, it behooves practitioners and scientists to assure themselves of the best hypotheses possible. One approach is to understand how logical structure gives rise to hypotheses. Examples include analyzing the nature of causation, a causes b, which fundamentally is the practical syllogism. One may start with the observation of b and then wonder what could be the cause, a. Often, one has a potentially causal agent, a, and one looks for possible consequences, b’s. Once causes, such as a, and consequences, such as b, are found, the hypothesis is constructed that a implies (causes) b. The experiment becomes if a implies b is true and b is true, then a is true, which is the scientific method (as well as the Fallacy of Confirming the Consequence). Other sources of hypotheses include metaphor, which is the Fallacy of Pseudotransitivity, which is an extension of the Principle of Transitivity. These issues are taken up in Chapter 5.

Rationalist/​Allopathic Medicine Versus the Empirics From the discussion given earlier, it is clear that hypotheses do not spring forth fully formed as Athena from the head of Zeus, but instead have genealogies and evolutions. At the very least, this is due to the A Priori Problem of Induction and the development of logic, in its evolutionary sense, to deal with uncertainty. But the quandary surrounding hypothesis generation is far more deeply rooted and relates to the fundamental epistemic question of how to deal with the variety of phenomena. The critical question becomes whether variety represents variation around canonical forms or each anew and independent. The answer turns on causality: in one sense, the presupposition that variations in causation act on the canonical form to produce variation. This is the presupposition inherent in the rationalist/​allopathic tradition most descriptive of today’s medicine. The other sense holds that there is no explicating theory of causation, and, consequently, no canonical form can be presupposed. The latter argues that each phenomenon is taken anew and as independent and is the basis for the empiric traditions in medicine, more typical of what is called today “alternative medicine.”

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Medical Reasoning

The fundamental epistemic question also turns on consideration of the economy of both the explanations and the things to be explained. For example, if one allows as many explanations as there are things to explain, then one is at risk for the Solipsism of the Present Moment. However, if one desires a set of explanations that have a smaller number of elements than the set of things to explain, how is this economy established? How is the reduced set of explanations mapped onto the greater number of things to be explained? As will be developed in Chapter  6, the answer to this question determined the schism in medicine between the rationalist/​allopathic practitioners and the empirics. That battle came to a head in 1910, with publication of the Flexner Report, but it has yet to be resolved (Chapter 6). It would seem that the remarkable success of modern medicine, which is predominantly rationalist/​allopathic in nature, demonstrates the fundamental soundness of the rationalist/​allopathic approach over that of the empirics. At the very least, this is a Whiggish interpretation of history written by the victors. Arguably, until the mid-​1900s, there is nothing in the results obtained by the rationalist/​allopathic physician that could unbiasedly champion that approach over that of the empirics. Rather, the dominance in the early 1900s was more political than scientific (Chapter 6). The Rationalist/​Allopathic approach is not without difficulties. Not only is there prior stipulation of an economy of explanations, but there also correspondingly is the necessity of economizing the set of things to be explained in order to establish a mapping or correspondence between the two. Any clinician dealing with the International Statistical Classification of Diseases and Related Health Problems (the ICD) and similar codes for medical diagnoses quickly realizes that often it becomes a process of trying to “fit a square peg in a round hole.” The problematic nature of economizing the thing to be explained—​for example, a disease to be explained—​is seen in the recent controversy regarding the diagnosis of Parkinson’s disease in the face of neuroimaging suggesting no deficiency of dopamine. It becomes even more difficult when the nature of the diagnosis determines what treatments are offered, with the serious risk of treating those who would not benefit as well as not treating those who would benefit. These issues are discussed in detail in Chapter 6. While it might be relatively easy to dismiss the epirics, it is not so easy to dismiss their choice in the face of the fundamental epistemic question when confronted with a diversity of phenomena—​the things to be explained. The rationalism of an explicit logic of rationalist/​allopathic medicine may merely be a chimera. The remarkable variations in possible geometries when Euclid’s fifth postulate is abandoned or the fundamental failure of any formal system of logic to demonstrate, by derivation, reasonable logical theorems, demonstrated by Gödel’s incompleteness theorems, argue against the hubris of rationalist/​allopathic medicine.

Introduction

Science and Scientism The response to the fundamental epistemic question from the variety of phenomena results in an ontological choice. The universe in which clinicians and biomedical scientists operate is just such that a great economization of explanations and things to be explained is possible and rationalist/​allopathic medicine results. Alternatively, the universe is just such that an economization is not possible but only an illusion, with empiric medicine results. The choice of ontology does much to reduce uncertainty as to the nature of medical reality, at least in the minds of those holding these ontologies, but at the risk of hubris. In the battle between the choices in response to the fundamental epistemic question that ultimately resulted in the schism between rationalist/​allopathic and empiric medicine, the battle was not turned in favor of the rationalist/​ allopathic physicians until the Flexnerian revolution in medical schools. In the late 1800s, homeopathic practitioners outnumbered rationalist/​allopathic physicians (Chapter 6). Prior to the first quarter of the twentieth century, perhaps the greatest contributions to the actual health of patients were vaccination and public health measures. It is not clear whether these significant practices favored one approach over the other. However, it would seem that the success of vaccinations favors the empiric medicine approach. The motto of the empirics is “like treats like.” Cowpox is “like” smallpox: giving cowpox prevents smallpox. The tension between the rationalist/​allopathic physicians and the empirics persists, as evident in the initial rejection of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-​5) for psychiatric disorders in 2013 by then-​director of the National Institute of Mental Health, Thomas Insel, because he believed it lacked scientific reductionist rigor and instead continued to emphasize a phenomenological approach (discussed in greater detail in Chapter 10). The epistemic notion presupposes the ontological notion of science; that is, the universe is just such that the epistemic notion that defines the practice of science will be ultimately and completely successful—​if not now, surely in the future. Such a faith defines scientism, as opposed to science. Yet, even within science, there are inklings of doubt. Not just the issue of the testability of the Big Bang theory, but also including the implications of the Gödel incompleteness theorem that supervenes on any formal logical system, among which one can reasonably count science and insights into chaos and complexity. One could argue that faith in the notion that science will be completely successful in explicating all of reality is an act of hubris. Deciding policy on acts of hubris is not without risks. For example, some biomedical researchers opposed the creation of the code of ethics by the AMA in 1847 because they believed that advanced science would obviate the need for ethics, and the imposition of ethics was but an undue burden and restraint.

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Events even a century later, such as the Tuskegee Syphilis Study, demonstrate the hubris on the part of those biomedical researchers (Chapter 6). The same critique can be applied to basic biomedical research finding vindication in the contributions to medical practice. The track record for contributions by basic biomedical research in the past is questionable, and the potential of present-​ day contributions is problematic (Chapter 15). It is science that should determine actions, not unbridled scientism.

Perspective This effort begins with the quest for certainty beginning at the point of greatest certainty—​deductive logic. However, it quickly becomes apparent that absolute certainty comes at the expense of utility in generating new knowledge. In order to gain new knowledge, logical fallacies must be used in a judicious manner. Furthermore, probability and statistics were developed with the effect, if not intent, to render the use of logical fallacies, such as partial and practical syllogisms, more certain as they create new knowledge. In many ways, the errors and problems of medical practice and biomedical research are consequent to the injudicious manner in which logical fallacies are used. Subsequently, attention was turned to the fundamental epistemic question of explicating the potentially infinite variety or diversity of phenomena. Addressing the fundamental epistemic question in the context of health and disease leads to an epistemic choice: either a reductionist approach based on combinatorics of more fundamental premises and propositions (axioms and rules of inference) or an inductive approach that makes no claims as to underlying mechanisms. These choices lead to the rationalist/​allopathic and empiric medical systems, respectively. While perhaps somewhat narrow, this perspective carries the views through the full range of medical practice and biomedical research. This is not to detract from other important perspectives, but only to point out the contributions of the perspective of logic and epistemic choice in response to the fundamental epistemic question. I believe that the perspective of logic and the response to the fundamental epistemic question is critical and will be productive when applied prospectively. The perspective from logic progresses through the search for tools for certainty while mitigating the price in utility that must be paid. The perspective from logic and epistemic choice also demonstrates its limits and thus puts a spotlight on the other important complementary and necessary perspectives, if for no other reason than to avoid scientism, which would reduce what it is important to appreciate in life. With the introductions in place, let’s dance.

2

What Are We to Make of Reasoning in Modern Medicine?

What do you think of clinicians?” I observe the Phisician, with the same diligence, as hee the disease; I see hee fears, and I feare with him, I overrun him in his feare, and I go the faster, because he makes his path slow; I feare the more, because he disguises his fear, and I see it with more sharpnesse, because hee would not have me see it.   He [Phisician] knowes that his feare shall not disorder the practise, and exercise of his Art, but he knows that my fear may disorder the effect, and working of his practise.   —​Donne (1624) YOUNG RESIDENT: JOHN DONNE:

In this quote, John Donne eloquently describes what is fundamental to every patient, family member, and caregiver—​the need for certainty to banish fear. That fear is palpable. The power that patients place in the certainty of diagnosis has been the source of power for physicians since ancient times. It is the diagnosis that advises the future (prognosis) and treatment. Physicians have guarded the right to diagnosis since the ancient Greeks practicing according to the Hippocratic Oath, which left the actual treatment to others, such as apothecarists (now pharmacists) and surgeons. Hippocrates admonished physicians for “cutting for stones,” thought to refer to the surgical removal of urinary bladder stones. Physicians in 1704 sought to bar apothecarists from rendering diagnoses, as seen in the Rose Act (Cook, 1990), and similar concerns were raised in the Code of Ethics of the American Medical Association of 1903. This asymmetry of authority has been persevered. However, the sense of authority central to this effort is the power of suasion derived by a projected sense of certainty in the understanding of disease. To be sure, there are authorities vested in clinician societies and governments and privileges granted by contract, license, convention, or custom. To be sure, there have 21

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been remarkable upheavals in the notion of authority. The United States has seen the rise of modern Health Maintenance Organizations (HMOs) following the Health Maintenance Organization Act (HMO Act) of 1973 and the Employee Retirement Income Security Act (ERISA) of 1974, whose power was only slightly mitigated by the Patient Protection and Affordable Care Act (ObamaCare) of 2010. Even the patient–​physician relationship has undergone significant changes, in which concepts of patient autonomy and advocacy have changed the authority of clinicians to one of influence other than by suasion. These issues deserve and are beginning to receive the scholarly attention they deserve (Charon, 2006; Halpern, 2001; Montgomery, 2006; Solomon, 2015), but they are beyond the scope of this book, other than as a brief introduction and, fortunately, are not necessary for our purposes here (although addressed briefly elsewhere).

Certainty, Knowledge, and Understanding Authority conveyed by the certainty of understanding is the primary focus here. Note that the term “understanding” is used rather than “knowledge.” This is a distinction that often escapes clinicians and scientists. The term “understanding” admits of some tentativeness compared to the certainty typically connoted in the term “knowledge.” Understanding goes beyond knowledge if one considers that knowledge is a product of human sensibilities (perceptions of matters of fact); yet these sensibilities had to be mediated by understanding for knowledge to be derived. For Kant, understanding utilizes a priori concepts, called “categories,” that allow the apprehension of perceptions afforded by the senses. In other words, one sees what one is prepared to see, and it is the a priori concepts that then enable to one to see—​or not see, in the case of radiologists missing the image of a gorilla imposed on chest computed tomography (CT) scans (Drew et al., 2013). Understanding provides the framework whereby experiences become knowledge. Understanding provides the theoretical constructs that “fill in the gaps” (interpolation) or extend the range (extrapolation) of an incomplete knowledge. This is critical because the obligation to beneficence requires clinicians to act positively in the situation of the individual patient, even in the face of incomplete knowledge (an idea explored in Chapter 3). There is another component of the Kantian notion of understanding, one found in his Transcendental Deduction, particularly the concept of Transcendental Apprehension in his Critique of Pure Reason (1781); this is the synthetic a priori. This concept is introduced in the prior understanding necessary for subsequent knowledge, as described previously. The notion of the synthetic a priori is complicated and controversial, but it is an agency that actually allows the perception of the sensible world that figures importantly in

What Are We to Make of Reasoning in Modern Medicine?

induction, which is held central to medical diagnosis and scientific research. This is reflected in the A Priori Problem of Induction (discussed in detail in Chapter 5). For example, what is it about a group of patients that allows them to be assembled so that features they have in common come to define a specific syndrome? For example, patients with weight loss, excessive thirst, excessive urination, and sweet-​tasting urine are generalized to create the diagnosis of diabetes mellitus. Subsequently, it was found that these patients had elevated blood sugars, so now the generalization is made that elevated blood sugars suggest the diagnosis of diabetes mellitus. However, when selecting the original group of patients, why was not hair color, eye color, or right-​or left-​handedness incorporated into the selection criteria? Clearly, there must have been some prior understanding that these factors are not relevant. The authority by suasion available to the clinician derives from his or her degree of certainty, but often certainty is not driven by the reasonableness of the clinician’s understanding, as most patients are in no position to judge. Nor is certainty exclusively driven by knowledge of the facts as these often are incomplete. However, certainty is conveyed by the clinician, and patients have a “sense” of it. This book attempts an exploration of certainty in biomedical research and the practice of medicine. This exploration is analytical in an epistemological sense and historical in how these epistemic issues play out over time. It is a descriptive (how things are) and normative (how things should be) assessment. The analytical begins in logic, but perhaps in a very different understanding of logic whereby logical fallacies, typically shunned, now are embraced. Judicious uses of logical fallacies are necessary for new knowledge, while injudicious uses have been the source of woe for all. The key to good medical reasoning requires understanding what constitutes judicious use of logical fallacies. The analytical consideration proceeds from the epistemic condition in which medical reasoning is situated. First is the vast and bewildering array of phenomena presented by patients. It is more than just a truism that no two patients are exactly the same. The ontological challenge is to determine whether this variety represents variability or diversity—​this is the fundamental epistemic question. The notion of variability, as opposed to diversity, implies that individual manifestations are the consequence of some varying combination of an economical set of underlying principles, as discussed in Chapter 1. The economical sets become universals and general principles, just as in science, and generate claims to certainty by regular/​ rationalist/​allopathic clinicians (discussed in greater detail in Chapter 19). The notion of diversity—​that each individual is ontologically different and cannot be reduced—​is the basis for beliefs of the Empirics. Each individual patient becomes an n of 1 experiment in understanding. These distinctions are discussed in greater detail in Chapter 6. The Dogmatics hold that diagnosis and treatment follow from a direct application of learned texts, thus merely matching the patient to the dictates of

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the learned texts. Not much more will be written about the Dogmatics except to point to the striking similarly of this position to the position of current advocates for checklists and decision trees to instruct diagnosis and treatment, a possible form of “cookbook medicine.” The rationalist/​allopathic schools use of economical sets of mechanisms and explanations presupposes greater complexity of the inner workings of humans compared to their observable behaviors, thus creating the Inverse Problem. This problem occurs whenever there are multiple mechanisms that can produce the same phenomena. Consider fever:  a great many different causal mechanisms produce the same fever, and there is nothing unique to fever that allows one to determine which cause(s) is operational. As will be seen, the Inverse Problem follows a fallacy of logic called the Fallacy of Limited Alternatives, which is of the form if a or b or c causes d, and b and c are found false, then a must be the cause of d. This fallacy of deduction can be extrapolated to probability theory to create the Gambler’s Fallacy (see Chapter 4 and Appendix B, available at www.oup.com/us/medicalreasoning). The Inverse Problem is also related to the Duhem–​Quine thesis in logic, which holds that if the conclusion of any argument is found false, such as a diagnosis or treatment decision, then one or more premises must be false (e.g., the patient really did not have the inclusion criteria) or one or more propositions (e.g., that a test would be positive if the patient had the disease) must be invalid. The problem is that one cannot know which premise is false or which proposition is invalid from just the false conclusion that the patient had the disease. The risk is that some premise or proposition may be falsely blamed, thus leading to invalid remedial efforts. Hypothesis generation is critical to gaining new knowledge, such as in medical science, but also knowledge of the patient’s diagnosis, for example. Hypotheses are the starting material in the scientific method and for the hypothetico-​deductive approach to medical diagnosis. Clearly, any experiment or diagnosis is only as good as the hypothesis; yet relatively little scholarly attention has been focused on good or proper methods of hypothesis generation. Typically, hypothesis generation is related to psychology, politics, or happenstance. As will be demonstrated, hypotheses can be generated robustly from the judicious use of logical fallacies such as the Fallacy of Pseudotransitivity and the Fallacy of Confirming the Consequence (Chapter 5). A risky route to robust hypotheses is induction because of the A Priori Problem for Induction (Chapter 5). These themes, constituting principles of applied epistemology, will be demonstrated repeatedly in different contexts throughout the remainder of this book. Such repetition is necessary for the contextualization of applied epistemology to provide explication of these principles. Such contextualization is just as necessary for applied epistemology as it is for applied biomedical ethics (Beauchamp and Childress, 2013). But first, is all this effort necessary?

What Are We to Make of Reasoning in Modern Medicine?

Track Record on Beneficence While the average life expectancy of a person in the United States was less than 65  years in 1940, it now is 78  years. Most serious childhood infections have been eliminated or the risk reduced greatly. Many cancers now are curable. Even with the epidemic increase in “lifestyle” diseases, such as type II diabetes and those attributable to tobacco use, the armamentarium provided to clinicians by medical science to treat these illnesses has increased dramatically. All these advances would seem to vindicate the remarkable changes in medicine that occurred in the late 1800s and early 1900s, coincident with the Flexner Report of 1910 (Flexner, 1910), and to justify the investments made by society in government-​sponsored biomedical research. In the United States, the budget of the National Institutes of Health is more than $30 billion. Yet, in 2000, the US Institute of Medicine reported that medical errors caused between 44,000 and 98,000 deaths every year in American hospitals (Kohn et  al., 2000). While the percentages that were preventable is open for debate (Brennan, 2000), in 2016, medical errors were estimated to be the third leading cause of death in the United States (Makary and Daniel, 2016). Wide geographical variances in specific treatments would appear to go beyond demographic differences (e.g., the use of cesarean delivery of babies; Baicker et al., 2006). To be sure, a great many complex factors contribute to this wide variance, including many not directly medical. Nonetheless, whatever the causes, such a variance clearly demonstrates problems in medical reasoning. Unfortunately, the conduct of medical practice seldom allows for recognition and understanding. Many medical centers have morbidity and mortality conferences whose intended purpose is to review cases that went wrong. This certainly should provide raw material for the careful analysis of medical reasoning, but studies have shown that the issue of errors in reasoning is rarely addressed (Gore, 2006; Pierluissi et al., 2003), despite the fact that most governments provide legal protection against self-​incrimination that could ensue from such important but open discussions. To be sure, clinicians could respond that it is not fair to hold clinicians responsible for imperfect access to care, discriminatory allocation of resources, cultures that promote unhealthy lifestyles, and the not always reasonable demands of patients, family members, and caregivers or the misadventures reported in medical reasoning that involve only a few clinicians. Yet, arguably, wide and unaccountable differences exist among different physicians, for example, in the way general primary care and specialist physicians treat the same kinds of patients. Nonneurologists are more likely to prescribe levodopa to patients early in the course of their Parkinson’s disease compared to movement disorders neurologists who are more likely to prescribe dopamine agonists (Swarztrauber et al., 2006). This fact is not to say which approach is better in the treatment of early Parkinson’s disease, but, clearly, differences exist in the

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modes of thinking. However, it cannot be that there is one type of patient with Parkinson’s disease who presents to the non–​movement disorders neurologist and a different type who presents to the movement disorders neurologist.

Reproducibility in Biomedical Science If healthcare outcomes are poor reflections of medical reasoning, where, then, is one to turn to assess the strength of medical reasoning? Certainly, allopathic medicine could argue that biomedical science and its derivative technologies and treatment algorithms are a better testimony to medical reasoning in ideal settings. Yet more than 20% of preclinical animal-​based studies in cancer therapies are not reproducible and thus are suspect (Begley and Ellis, 2012; Prinz et al., 2011). Only 68% of studies in experimental psychology retain statistical significance on replication (Open Science Collaboration, Psychology, 2015). However, this intellectual contagion spreads to every biomedical research study, and, if one were honest, every publication would have to be suspect until it is demonstrated reproducible. The governmental postapproval recall of drugs, biologics, and devices also is evidence of irreproducibility. In the first 8  months of 2014, the US Food and Drug Administration (FDA) recalled 836 drugs; in 2013, 1,225 drugs were recalled (http://​www.raps.org/​Regulatory-​Focus/​News/​2014/​08/​11/​20005/​ Number-​of-​Drug-​Recalls-​Surges-​at-​FDA-​Led-​by-​Mid-​Level-​Concerns/​). With respect to medical devices, 91.5% of devices were not recalled within 5  years of approval. The percentage of patients who received benefits from the 10 top-​selling new medications was approximately 20%. An honest assessment is that these treatments are only “20% reproducible.” With regard to medical devices, the Institute of Medicine wrote, “Device design, that is, failure of a device to perform as intended despite the product’s meeting all its design specifications, accounted for 28.4% of recalls” (Institute of Medicine [US] Committee on the Public Health Effectiveness of the FDA 510(k) Clearance Process, 2011). However, it must be noted that FDA recalls could suffer ascertainment problems and actually reflect only the most egregious examples. The response of some administrators of the scientific enterprise, such as journal editors and grant reviewers, has been to argue that lack of transparency and poor statistical techniques are the causes of nonreproducibility, but it is not clear that these are sufficient explanations. First, individuals considered experts in their fields vetted grants providing for these irreproducible studies and vetted their subsequent publications. Presumably, their knowledge of the science and the statistics is sound—​or at least as sound as reasonably possible. It is difficult to see that a more rigorous application of the current methods of science or statistics will solve the problem. Second, these studies make positive claims and thus are analogous to type I errors (type I errors

What Are We to Make of Reasoning in Modern Medicine?

claim a difference when a difference truly does not exist). Unaccounted for are type II statistical errors (type II errors do not find a difference when a difference truly exists).

Approach in this Text and an Emphasis on Logic in Its Widest Connotation The argument to be advanced herein is that the problems of medical reasoning reflected in irreproducible biomedical research and medical errors, to the extent that they are avoidable, are symptomatic of the state of medical reasoning. However, medical reasoning goes far beyond inferences from biomedical research and clinical benchmarks, as would be expected given the enormous complexity of the very human endeavor of practicing medicine. While modern science may have succeeded, to a questionable degree, to be above the human fray by claiming to be value-​free, those caring for patients are deeply involved in issues of value. The practice of medicine is complicated by the different sets of values held among shareholders, those with influence, and stakeholders, those affected. Each and all are competing, with the physician or healthcare professional caught in the middle. Attempts to regulate medical practice from an economic–​utilitarian perspective clearly clash with the patient’s and society’s expectations, as well as with the role, authority, and responsibilities clinicians see in themselves (discussed further in Chapter 19). Many of these issues have been the subject of excellent critical analyses. These include Jodi Halpern’s From Detached Concern to Empathy: Humanizing Medical Practice (2001), Kathryn Montgomery’s How Doctors Think: Clinical Judgment and the Practice of Medicine (2006), Rita Charon’s Narrative Medicine: Honoring the Stories of Illness (2006), and Jerome Groopman’s How Doctors Think (2007). This text takes a different focus, one based on epistemology, particularly logic and the responses to the fundamental epistemic question of diversity versus variation. Moreover, this text views logic from what some might consider a somewhat different perspective. Beyond deductive and inductive logic, the argument will be advanced that the use of logical fallacies is necessary and needs to be embraced, albeit judiciously.

Evolutionary Epistemology and Logic The epistemic approaches to gain certainty and new knowledge evolved in response to human needs in the context of a specific epistemic environment. For the purposes of this book, logic is synthetic and organic. Indeed, as will be demonstrated, the very choice in response to the epistemic conundrum determines the school of medicine held, for example, between the Empirics

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and the rationalists/​allopathic physicians. The logic proposed here is organic in that it arises from human needs, and it is synthetic in that it is a response created by humans. As will be seen, this form of logic goes well beyond deduction. The metaphor of evolutionary biology illuminates the notion of organic and synthetic logic. Many accounts of evolutionary biology focus on the generation of new species as the determinant of the course of biology of the species. But that is only true in a reactive way. It is the change in the environment that determines the trajectory of species change. Mutations that generate different individual species happen randomly, more or less. Changes in the environment determine which of the random and spontaneous mutations survive and procreate. Focusing on individuals creates a false sense of agency for the individual, which often has been confused as a teleological purpose. The universe did not evolve in order to create humans as the penultimate realization of purpose, a myth dispelled by Steven J. Gould in his book Full House:  The Spread of Excellence from Plato to Darwin (1996). Evolution, then, is synthetic—​it is not an a priori necessity driven by teleology or purpose. It is organic in that it responds to a need, in this case a change in the environment and its coincidental consequences for survival and procreation. Logic, and its extensions to useful logical fallacies—​probability and statistics—​is a response to a need to act regardless of whether this is explicitly realized. Given the complexity of human health and disease, is it then any surprise that humans would invent, explicitly or implicitly, some mode of logic to aid in the decision-​making process? The first chapters of this book explores the nature of the problems that medicine is required to confront and its differences from other knowledge disciplines. The second part of this book explores how the nature of medical decisions has played out in the history of medicine, particularly in response to epistemic conundrums. The final part of this book explores how the implications of the complexities of medical decision-​making play out in the care of patients. In evolutionary biology, the change over time of a species becomes a probe by which to understand the dynamics of the environment—​witness the change in the coloration of a species of moth in northern England with the environmental change caused by pollution. The moth went from white to black as industrial soot covered the landscape. Understanding the dynamics of evolving medical reasoning provides an opportunity to be proactive rather than reactive in developing better patient care. In a sense, the majority of this text can be considered a Critique of Pure Medical Reason (discussed in detail in Chapter  19), with all due apologies to Immanuel Kant’s Critique of Pure Reason (1781).

What Are We to Make of Reasoning in Modern Medicine?

Use and Misuses of Logical Errors As will be seen, the extent and varieties of logical errors is considerable, arguably accounting for a great many of the problems in medical reasoning (discussed in detail in Chapter  3). For example, the scientific method and its analog in the hypothetico-​deductive approach to diagnosis are essentially the Fallacy of Confirming the Consequence. An actual construction of hypotheses, whether in science or medical diagnosis, often involves the Fallacy of Pseudotransitivity. The failure to diagnosis, a prominent accusation of medical malpractice, reflects the Fallacy of Limited Alternatives and the Gambler’s Fallacy. The lack of reproducibility evidenced in the postapproval recall of drugs and devices and the abandonment of therapies validated scientifically will be shown often to be due to the Fallacy of Four Terms in syllogistic reasoning. Evidence-​based medicine will be seen as inherently limited in its applicability to the practice of medicine because of the Second Law of Thermodynamics as applied to Information (as discussed in detail in Chapter 16).

Applied Epistemology Viewed in these terms, logic, logical fallacies, probability, and statistics are less rule-​following and are more creations made to achieve a purpose. Understanding medical decision-​making in this context can help philosophers play an important role in medical care and research in their role as applied epistemologists. The epistemic situation is analogous to biomedical ethics. Philosopher ethicists are frequently employed by major healthcare provider organizations. Their role is to help clinicians resolve ethical problems as they arise. For example, a cardiologist may seek the advice of a biomedical ethicist. The ethicist typically is not a cardiologist. However, the ethicist is an expert in ethical principles and moral theories and has experiences that serve as useful precedents. The collaboration between the ethicist and the cardiologist first contextualizes the ethical principles and moral theories that then allow for an ethical and moral analysis and resolution. Epistemologists are trained in logic and in the theories of knowledge. Many have strong backgrounds in science, particularly the history of science, that lend some understanding of medical science, which is important in modern allopathic medicine. This knowledge and skill could be brought to bear on interpreting evidence and circumstances that relate to the use of biomedical science in patient care, as well as in the design and conduct of clinical research. The hope is that this text will impress upon clinicians and researchers the importance of their own understanding of medical epistemology and the potential value of consulting with applied epistemologists.

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Descriptive, How Things Are, Versus Normative, How Things Should Be This text takes both a normative and a descriptive approach to analyzing and understanding medical knowledge and its uses. The normative approach is possible in following from simple deductive logic and its extension to logical fallacies, probability, and statistics. However, a descriptive, particularly historical, approach is necessary because the logic used in the normative approach is evolutionary, arising from the environment in which medical reasoning is exercised. A normative approach based on logic and its various extensions may seem limited, and it may well be. It is hoped that the remainder of this book will demonstrate that a great deal can be done. Certainly, medical science follows from the application of logic and its extensions, and, at least in allopathic medicine, biomedical science helps drive decision-​making. Even ethical aspects of medical decision-​making can be examined by logic and its extensions. For example, Beauchamp and Childress (2013) based their program of ethics on common morality, discussed in greater detail in Chapter  19. The concept of common morality is derived descriptively as the consensus of all reasonable persons distilled into a set of ethical principles. The distillation follows from John Stuart Mill’s Method of Induction, particularly the Method of Differences—​ the culling of noncommon morality when unreasonable persons are excluded. Subsequently, the ethical principles can be used in a logic that is normative. Many other aspects of medical practice and research demonstrate this type of synergy between the descriptive and the normative. For example, physicians are observed acting in such a manner that the list of symptoms and signs is matched against a list of candidate diagnoses, a form of pattern recognition. Some medical schools seek to enable students to mimic pattern recognition. The descriptive, what is observed, becomes the normative, what should be done. The Strong Social Program in science and the psychology of science argue for a similar process in biomedical science. Such postmodernist critiques to biomedical science and practice will not be pursued here, but the interested reader is referred to Morris (2000).

The Dichotomization of Medicine Between Science and Art There always has been a distinction made between what is held as the science (episteme) and the art (techne) of medicine, although Miriam Solomon has made cogent arguments to the unproductive nature of such a distinction (Solomon, 2015). However, prior to a brief introduction of this topic, it is important to specify biomedical science as it is attributed to allopathic physicians, both by proponents and antagonists to the allopathic tradition (see Chapter 11). It will be argued that the division between allopathic (also called

What Are We to Make of Reasoning in Modern Medicine?

rationalist, regular, and heroic) and empiric (including irregular, homeopathic, herbalist, and Thomsonian) physicians reflected the two extremes of dealing with the fundamental epistemic question:  how to understand the individual patient as either variation of an economical set of underlying mechanisms or as diversity, such that no generalization is possible, and each individual patient must be taken as a new experience to be explored. A practical outcome is that allopathic physicians seem to dispense with the whole phenomena of the patient and instead focus on an economical set of phenomena that map onto an economical set of underlying mechanisms defined by biomedical research. The Empirics, not abiding by such reductionism, are viewed as holistic. This last term seems to impart a sense of art and the “practical,” suggesting a distinction from science. This is sensed in Jodi Halpern’s From Detached Concern to Empathy (2001), Kathryn Montgomery’s How Doctors Think (2006), and Rita Charon’s Narrative Medicine (2006). These commentators stress that empathy, “practical” (phronesis) reasoning, and wisdom in listening to patients’ stories or narratives and in helping patients organize their experiences of their diseases are necessary for any physician or healthcare professional in understanding patients’ diseases. Some of these issues are addressed in Chapter 19. Forty years of practicing medicine has led me to agree wholeheartedly with the concerns raised by Halpern, Montgomery, and Charon. Certainly, clinicians would be more effective were they more empathetic, sympathetic, and respectful of the patient’s narrative. The question here is whether the recommendations of Halpern, Montgomery, and Charon relate more to establishing rapport rather than to understanding in an epistemic or ontological sense. Is not the “practical” reasoning—​at least some part of it—​described by Kathryn Montgomery just a way to deal with the nonlinearities of the patient dynamics and consistent with chaos and complexity (see Chapter 14)? Just because these nonlinearities are difficult for allopathic medicine and research does not necessarily lead to the conclusion that skills fundamentally of a different kind are required. The fact that the exact shape of an impending snowflake cannot be predicted does not mean that the rules of physics do not apply. Also, as will be demonstrated elsewhere in this text, much of the “practical” reasoning or wisdom actually is a colloquialism for formal logic and its extensions. For example, the clinical saw “when you hear hoofbeats, think horses, not zebras (unless you are in Africa)”:  this admonition is just Bayes’ theorem in disguise and is related to the Fallacy of Four Terms and the concern that a diagnosis that just seems too simple or facile may reflect an imprecise formulation of the Fallacy of Limited Alternatives. Experienced clinicians know that it is best not to change too many things at once lest one becomes confused. This is an implicit recognition of the epistemic difficulty called the Inverse Problem. Physicians learn implicitly to modify the recommendations of

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large-​scale trials based on the unique characteristics of the individual patient. This reflects the risk of the Fallacy of Four Terms.

Logic and Its Extensions Versus Science There is a tendency to conflate logic and its extensions with science, thereby not recognizing logic as relevant to medicine or biomedical research. It is possible to be logical without being scientific, just as it is possible to be mathematical without being scientific. To be sure, specific areas of mathematics map onto science, but the reverse is not necessarily true. The logical construction if a implies b is true and a is true then b is true need not be scientific if one holds to the distinction that what is scientific is experimentally testable, as the modern Popperian demarcation of science would hold (Popper, 1934). It may be just that the claim a implies b is true but is not verifiable experimentally. Science, as commonly practiced, is not logical. Indeed, the scientific method is the Fallacy of Confirming the Consequence as described previously and in more detail in Chapter 5. Hypothesis generation for application to the scientific method is the Fallacy of Pseudotransitivity. Yet these are necessary fallacies for the conduct of science in general and biomedical science specifically. With the distinction between science and logic made, then, clearly, there may be circumstances where scientific explication fails yet logic and its extensions prevail. Thus, the failures of biomedical science do not mean that there is no logic to medicine. One could say that there is danger in being too scientific, as Kathryn Montgomery might hold (2006), but this is not the same as saying one can be too logical. As described in the Preface to this book, Jerome Groopman describes a brilliant pediatric cardiologist who thought he was being logical and the result was a near catastrophe (Groopman, 2007, pp. 142–​149). Groopman’s conclusion was not to trust logic. Groopman described a case that involved a patient with severe narrowing of the mitral valve in the heart and a hole in the wall between the two halves of the heart. The cardiologist reasoned that if one closed the hole in the wall, there would be higher blood pressure in the left side of the heart because the blood pressure would not be reduced by the blood going into the right side of the heart. The cardiologist was quoted as saying “It has to be right, correct? It is very sound logic. But it’s wrong.” The child got worse when the hole in the wall of the heart was closed. Groopman’s explicit implication was to not trust logic. On closer analysis, this reasoning is illogical. The argument can be structured as if the hole in the heart is closed, there will be increased pressure in the left atrium (entryway to the left side of the heart), and if there is increased pressure in the left atrium, more blood will go into the left ventricle (the output of the heart) to give more blood to the body. The argument can be formalized as if a implies

What Are We to Make of Reasoning in Modern Medicine?

b and b implies c, then a implies c, where a = repair hole in heart, b = increased left atrial pressure, and c = more blood going into the left ventricle and then to the body. The argument has the form of the Principle of Transitivity. However, for any logical argument to yield true conclusions—​in this case, a implies c or repair of the hole in the heart implies more blood going to the left ventricle and then the body—​the premises must be true and the propositions must be valid. It would seem only a matter of simple hydrodynamics that increased left atrial pressure would equal increased blood flow to the left ventricle and then to the body, thus b implies c is true. The proposition that repair of the hole in the heart means (implies) increased left atrial pressure or a implies b is suspect. It follows that the increased blood volume because the repair of the hole is going to increase the left atrial pressure presumes that now the blood has nowhere else to go but through the mitral valve into the left ventricle, as would seem intuitive from the normal flow of blood through the left side of the heart. However, there is nothing, no valves, to stop the blood from going backward into the pulmonary veins and causing pulmonary edema, which is what happened. Thus, closing the hole in the heart does not necessarily mean increased left atrial pressure sufficient to force the blood through the mitral value into the left ventricle. Thus, the proposition that repair of the hole in the heart leads to increased left atrial pressure sufficient to increase blood flow through the mitral valve, or a implies b, is not true; consequently, the argument cannot provide any confidence that closing the hole in the heart will increase blood flow through the mitral valve into the left ventricle and then to the body. The failure of the argument has nothing to do with logic. The failure is due to the false proposition, for which the cardiologist is at fault, not logic. If someone chooses to use a hammer to saw a piece of wood, it is not the hammer’s fault that the piece of wood is shattered and not sawed. Thus, attribution of the cardiologist’s argument to logic is faulty and, if anything, demonstrates the clear need for physicians to understand logic appropriately. In proper analysis, the cardiologist was not being too logical; rather, he was not logical enough. However, the cardiologist may have been too scientific because the driving force in the chain of reasoning is causal: closing the defect in the wall of the heart will cause increased pressure in the left heart, which will cause increased blood flow through the mitral value. Note that the chain of reasoning is not the result of closing the defect in the wall of the heart is increased pressure in the left heart and is increased blood flow through the mitral value. When is, or other state-​of-​being verbs, is the linking verb, then the argument becomes logical. When the linking verbs are other than state-​of-​being verbs, such as the action verb cause, then the argument is no longer a syllogistic deduction (form of logic) but a practical syllogism, as described by Aristotle, and it no longer has the certainty of a syllogistic deduction.

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Medicine Beyond the Realm of Science but Within the Realm of Epistemology, Logic, and Logic’s Extensions The concerns of Halpern (2001), Montgomery (2006), and Charon (2006) are very important to the humane and effective practice of medicine and thus are important in medical reasoning. However, those concerns are rightfully directed at a scientism that underlies allopathic medicine (see Chapter 11) and not at logic and logic’s extensions. By scientism, it is meant that science will explicate all human concerns, and thus any nonscientific knowledge is superfluous and can be eliminated. As will be seen in Chapter  11, the triumph of scientism in modern medicine during the course of the nineteenth and early twentieth centuries was more political than based on any greater benefit to humans, at least at the time. There is another way in which science alone is not sufficient to inform the practice of medicine. The very tools science uses render any results problematic in their application to the individual patient. Science rarely draws inferences from a single observation, yet medical professionals are forced to do so with every patient they care for. In clinical research, a sample—​a subset of all subjects of concern—​is studied and the results are ascribed to the population—​the set of all subjects of concern. The extrapolation to medical decision-​making is that my patient is synonymous with the population, which is synonymous with the sample studied in clinical research. When interventions used in the observations of humans are not possible, then models thought in some way representative of humans are used. This is demonstrated in Argument 2.1. Argument 2.1 Major premise:  Subjects A (bridging term) get better with treatment B (major term). Minor premise: Population (minor term) is subjects A (bridging term). Conclusion:  Population (minor term) will get better with treatment B (major term). Minor premise:  My patient is from the population (note, not from the sample). Conclusion: My patient should receive treatment B. Critically thinking clinicians and biomedical scientists recognize that conclusions in each case are suspect even if they do not recognize the Fallacy of Four Terms. The value, however, of these logical fallacies is that they suggest testable hypotheses for scientific experimental vindication. It is critical that the medical professional recognizes that these arguments only have value in hypothesis generation. The use of such argumentation for verification or endorsement, which often happens, is a logical error that carries significant risk. Indeed, this is a likely source of the irreproducibility demonstrated by the recall of drugs, biologics, and devices.

What Are We to Make of Reasoning in Modern Medicine?

One can use an epistemic approach to determining the epistemic risk of such syllogistic arguments. In these cases, the epistemic risk is the difference between the bridging term used in the major premise and the bridging term in the minor premise. This difference can be assessed by the epistemic distance, how conceptually different are the two versions of the bridging term, and the epistemic degrees of freedom, the number of conceptual twists and turns needed to bridge the epistemic distance. These issues are discussed in greater detail in Chapter 13.

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Epistemic Challenges and the Necessary Epistemic Responses

Case 1: What do I have? PHYSICIAN: Hyperporcelainemia. PATIENT: Sounds bad. Do you know for sure I have it? PHYSICIAN: Good question. We’ll run some tests. Then we’ll know for sure whether you need treatment. PATIENT:

Case 2: What’s the mass of a Higgs boson? It is 125.3 ± 0.6 GeV/​c2. STUDENT: What does “plus or minus” mean? SCIENTIST: It means there’s a 95% chance that the weight is between 124.1 and 126.5 GeV/​c2. STUDENT: So you don’t know exactly? STUDENT:

SCIENTIST:

Case 1 involves a fact, or what has the epistemic status of fact. It leads to a dichotomous decision—​a diagnosis—​that ultimately results in an act—​testing. Hyperporcelainemia is a hypothetical condition characterized by an excess of porcelain in the blood. Either the patient has hyperporcelainemia or not. Even if the physician is unsure, the necessity of action requires a choice between the only two possible values. Regardless of the degree of confidence in the diagnosis or prevarication, either one treats or does not treat for the condition. Not treating is just as much a decision as treating. Nearly every physician and healthcare professional would say that partially treating would be the worst option. A clinician does not provide only 80% of a treatment because the clinician is only 80% confident in the diagnosis. This need, the necessity of action, is the evolutionary force that drives the epistemology of medical knowledge. 36

Epistemic Challenges and the Necessary Epistemic Responses

Contrast the situation of case 1 with that of case 2.  The second case involves a matter of a bit of speculation—​at least in that there is a confidence interval and not a confident single answer. The lack of an imperative to make a yes-​or-​no decision is a luxury for the scientist and one the clinician cannot afford. The 95% confidence interval in the case of the mass of the Higgs boson is somewhere between 124.1 and 126.5 GeV/​c2, although there is at least a 5% probability that it is outside the interval. One could argue central tendency; that is, the mean value of 125.3 GeV/​c2 reflects the ontologically true value of the Higgs boson, but that would be an article of faith (discussed in greater detail in Chapter 8). To make this claim, one would have to hold that any measurement that resulted in a value other than 125.3 GeV/​c2 was a consequence of measurement error, but there is no way to dismiss that different Higgs bosons just have slightly different weights, at least within the experiments. Importantly, physics will not be paralyzed if the mass of the Higgs boson is only known within a certain interval. Clinicians do not have the luxury of waiting for more results from more experiments or more research—​clinicians must make a decision for the patient in front of them, necessarily excluding alternatives. The physician in case 1 might have responded “there is a 95% chance that you have hyperporcelainemia.” While sounding more scientific, it comes no closer to offering an actionable decision. A 95% chance seems a virtual certainty, leading to a certainty for treatment—​or so it may seem. If, however, the treatment’s attendant risks are potentially severe, for example, in discomfort and cost, compared to the consequences of no treatment, then perhaps the chance should be 99%, 99.5%, or even 99.9% before the treatment is advocated. As will be discussed many times in the upcoming chapters, this question is not one for science, but one based on moral, ethical, sociological, political, and economic considerations that attend every clinical decision.

The False Choice Between Universal Scientific Judgment and Particular Common Sense Judgment As will be demonstrated repeatedly, it would be an error to hold medical decision-​making as simply being a different kind or category than decision-​ making in science. Strictly allopathic physicians have and would argue that such a characterization of medical decision-​making would give license to a less exacting standard of knowledge—​the kind expected of science, particularly physics (Chapter 6). It will be argued that medical reasoning, practical or not, is nearly identical to scientific reasoning, with but a few key distinctions. Some clinicians and academicians tout evidence-​based medicine as once again making the claim for scientific medicine. As later historical analysis will demonstrate, medical practitioners were never wholly won over to purely scientific

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medicine. The resistance to scientific medicine, going back centuries, is of the same cloth as the current resistance to evidence-​based medicine. To make the diagnosis of hyperporcelainemia requires the patient’s serum porcelain be higher than normal. Medical research then establishes the confidence interval for normal serum porcelain levels. Fundamentally, there is no difference between establishing the confidence interval for the mass of the Higgs boson and the confidence interval for normal serum porcelain levels. Also, just as the physician has to decide the range of serum porcelain levels that constitute normal in order to make the ontological claim of excessive porcelain, the physicist has to decide what range of weights of the Higgs boson will be counted to make the ontological claim of the weight of the Higgs boson. It is not as though there is a different universe with different laws of physics for serum porcelain and the Higgs boson. It could be argued that practical medical reasoning differs from scientific reasoning in that medical decisions very often are not bracketed between scientifically empirical evidence. Even the utility of interpolation between scientifically empirical data points to the individual patient often is not available. For example, consider a correlational analysis between the serum porcelain concentration and the presence of symptoms. The individual’s serum porcelain concentration may not be exactly the same as any of those subjects whose concentration and symptoms were measured and used in the correlation. Nonetheless, the individual patient’s risk of symptoms can be read from interpolation of the individual’s concentration from the data bracketing it. But how is that different from any scientific correlation? Such scientific correlation still requires interpolation if any regression line is applied. And, like the central tendency, a regression line is considered the truest measure of the ontology, within certain limits. Interpolation and extrapolation are discussed in detail in Chapter 10. As noted, 95% of all measurements of the mass of a Higgs boson fall within the range of twice the standard deviation of 0.6 GeV.c2 on each side of the mean measurement of 125.3 GeV/​c2. Experimental confidence is five standard deviations, or 5 sigma. The explanation for why 5 sigma was selected over other numbers-​sigma appears to lie in the fact that scientists who had selected 3 sigma in the past later found their claims proved false (see “5-​Sigma—​ What’s That?,” Scientific American Observations Blog, July 17, 2012, at http://​ blogs.scientificamerican.com/​observations/​2012/​07/​17/​five-​sigmawhats-​that/​). It is not clear how dissimilar this rationale is to the physician’s embarrassment at making the wrong diagnosis. Both medicine and science seek a certain ontological description of their respective interests in the same universe. Both science and medicine, at least allopathic medicine, employ the same methods and standards in an effort to achieve their respective certainty in their ontological notions. To this point, there is very little or no qualitative difference between practical medical and

Epistemic Challenges and the Necessary Epistemic Responses

scientific decision-​making, although often there are quantitative differences. That practical reasoning often appears to be unscientific may be more a matter of the particulars of the data and analysis than of a general epistemic or ontological kind. After this point, however, medical and scientific decision-​making diverge (as discussed further in Chapters  4 and 6)  and the consequences are enormous. Science focuses on the central tendency, which is taken as the truest vision of the ontology. Medicine must look to variance, thereby presupposing that the central tendency is not the vision of ontology, at least for the individual patient that the physician or healthcare professional is obligated to attend to. Elsewhere, it will be argued that the various epistemic methods employed by scientific medicine prevent an understanding of the individual patient’s ontology. At that point, medical and scientific decision-​making part company. Briefly, as this issue will be discussed in greater detail elsewhere, the methods for determining the central tendency, and thus scientific ontology, result in an irreversible loss of information according to the Second Law of Thermodynamics as Applied to Information. One cannot reconstruct the exact nature of the observations from which estimates of the central tendency were made. Yet it is the nature of the individual observations that are most relevant to the individual patient who demands a decision (discussed in detail in Chapter 8). In comparing medical practice to science and thus considering the appropriateness of science as a metaphor for medicine, one first has to ask which science: the science of Francis Bacon or that of Descartes? William Whewell, considered one of the founders of the British Association for the Advancement of Science (indeed, first coining the term “scientist”), was an ardent advocate for the Baconian approach (Snyder, 2011). Bacon’s approach is best characterized as constrained to strict experimentalism and is reflected in the motto of the Royal Society, Nullius in verba (Take nobody’s word for it). The contemporary alternative to Bacon’s empiricism was the rationalism of Descartes. The Cartesian paradigm argued that scientific knowledge could be deduced from first principles. One does not empirically verify Newton’s laws of motion or Maxwell’s laws of electromagnetism every time a new experiment is devised and executed. Nonetheless, one cannot say that these or analogous laws are not central to experiments and the claims inferred. The fact of the matter is that one does reason from these laws even if one does not verify them experimentally each time the experiment is conducted (Grosholz, 1991). Back to practical medical reasoning, it may seem unscientific from the perspective of Baconian science. After all, one does not have to reverify germ theory in a particular patient in the course of treating a strep throat. But one could argue that reasoning from the given of the germ theory is an example of science, even if it is only Cartesian science. Thus, the claim made and to be examined throughout this book is

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that medical reasoning is not antithetical to or immiscible in science, construed properly.

The Myth of the Inevitability of Certainty There lingers the temptation to believe that, for all intents and purposes, modern medicine has eliminated uncertainty, leaving clinicians to follow checklists or algorithms. This belief is false, as the chapters to follow will amply illustrate. The future may see medicine reduced to a binary-​valued decision tree whose branches consist of questions having yes/​no answers. Yet any such development would rest on fiat or majority rule rather than on scientific knowledge alone. At any rate, the fates of patients today continue to depend on the knowledge clinicians possess and their understanding of how knowledge is gained—​in other words, their competency as epistemologists. The most critical aspect of managing uncertainty is to fully appreciate the consequences of the binary decision. As will be demonstrated, wise medicine involves consideration of the consequences in their broadest terms, which include psychological, sociological, political, economic, ethical, and moral. As alluded to in Chapter 2, even these considerations may allow some degree of scientific analysis.

Clinicians’ Obligation to Reason Clinicians may state their ethical position based on what is expected of them. What has been expected historically has been based on descriptive ethics, actions taken by most clinicians in archetypical medical situations. But this notion is becoming more normative—​what reasonable clinicians should do. Courts of law have increasingly come to expect defenses predicated on reason over convention, particularly conventions suspected of being self-​ serving. These interests are normative—​what should be done. In light of this trend is the fact that, no matter how authoritative the professional organization publishing them, algorithms and checklists offer little protection from unfavorable court judgments. Note that this discussion of descriptive versus normative is different from that in Chapter  2, where “normative” implied a degree of analyticity in a modernist sense and “descriptive” implied a sociological analysis. Here, the issues of descriptive versus normative relate to ethical obligations on the part of clinicians. The argument to be made is that clinicians are obligated to use reason that unavoidably presses the importance of evolutionary logic and epistemology. The fact is that logic and epistemology are seldom considered in any explicit fashion, as in the medical peer-​review conferences described in

Epistemic Challenges and the Necessary Epistemic Responses

Chapter 2. In the end, clinicians must answer to themselves. I am continually reassured by the good intentions of aspiring clinicians who have elected to pursue careers that do not prove easy. There are easier ways to make a living. Primary care physicians, for example, experience lower returns on their educational investments than dentists, attorneys, and businesspeople (Weeks et al., 1994). Good intentions aside, something is wrong. An extensive medical records survey showed that only 23% of patients have their hypertension under control; 50% of people with depression are noncompliant with their medications, approximately 20% of elderly patients are prescribed inappropriate and potentially harmful medications, only 40% of hospital visits and 60% of office visits include smoking cessation counseling, only 67% of adults have had their cholesterol checked within the past 2 years, and 69% of patients who sustain heart attacks are prescribed beta-​blockers (Kelley et al., 2005). These relatively poor outcomes admittedly are due to a host of causes. It is impossible, therefore, to know how many outcomes are due specifically to flawed knowledge or epistemic errors. Experience has shown me that most clinicians are quite intelligent. Yet most errors committed by them are nonetheless errors of reasoning. At least some of these errors are structural, such as those stemming from professional training, which is essentially an apprenticeship, where the same errors pass from generation to generation.

The Evolution of Medical Reasoning and Misreasoning Errors in reasoning resemble the nature of their evolution, with at least two significant implications. First, the mechanisms underlying the evolution of misreasoning must be understood epistemologically in both basic science and in translation into a logic of reasoning. Second, understanding misreasoning likely will require the involvement of other disciplines than those that bear directly on patient care. Some medical and scientific publications, for example, hire statisticians as manuscript reviewers. Although statistics may be said to be subsumed under epistemology, reasoning errors include more than incorrect and inaccurate statistics, as discussed in Chapter 2. Furthermore, many statistical errors actually are based on logical errors and fallacies. If it is reasonable that medical and scientific disciplines call for the intervention of statisticians, then it is reasonable that they will also call for the intervention of epistemologists and logicians. Whether filled by a physician or a healthcare professional, the role of a medical epistemologist should find a place in the practice of everyday medicine. A physician or healthcare professional is arguably an epistemologist each time a decision is made. The only question is whether she plays this role well. Medicine is moving rapidly toward team-​ oriented approaches. It may be that an individual physician is simply unable to know or do it all. But the

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notion of a team is changing rapidly: Fading is the role of a physician as team captain; the physician now becomes an adjudicating authority, such as a judge. As such, a physician need not be an expert in all aspects of medical care. In fact, she may not even need any formal training or experience specific to the case she must adjudicate. But she must be an expert in rules of evidence because her primary role is that of a fact finder. The physician becomes the arbiter of claims and decisions by team members. Because finding facts is clearly an epistemic exercise, an adjudicating physician or healthcare professional is an epistemologist, good or bad. Advances in medicine and ethics are changing the adjudicating responsibility of clinicians. Clinicians may act as fact finders, but the ethical principle of respect for autonomy dictates that the translation of facts into decisions occurs in most instances at the discretion of patients or their surrogates (Beauchamp and Childress, 2013). Patients or their surrogates, therefore, depend on clinicians’ ability to weigh evidence. That is, they depend on clinicians being competent epistemologists. The inclusion of patients or their surrogates should be greeted with relief. Medical decisions, ultimately, are not solely scientific and must rest on value judgments based on the consequences of the choices made among available options. These consequences are determined by morality, ethics, economics, sociology, psychology, politics, and the patient’s unique context. No honest clinician can presume to be expert in all of these areas, enough to be authorized to make a decision for a patient. Research in the evolution of misreasoning is needed. Although researchers conduct psychologically based research into reasoning (Johnson-​Laird, 2008), understanding of the evolution of misreasoning remains critical to the acquisition and use of medical knowledge. The evolution of reasoning and misreasoning can be seen in the history of medicine (Chapter 6) in the same way that the fossil record remains critical to advancing the understanding of biological evolution. A fossil record is useful because the underlying fundamental mechanisms or principles are transcendental; that is, independent of a specific time or context. The very mechanisms of evolution that shaped the creatures that create today’s fossils also shape the creatures that will become tomorrow’s fossil. Similarly, the principles and mechanisms of medical reasoning are the same today as they were for Aristotle (384–​322 bc) and Galen (126–​216 ad), although the specific instantiations may be different. Thus, examining history’s thought leaders in medicine is relevant and may help physicians, healthcare professionals, scientists, psychologists, sociologists, philosophers, and others interested in or concerned about patient care today. Indeed, it is wise to heed past thinkers’ writings, if for no other reason than it is doubtful whether evolution has worked to make people today smarter than people of classical antiquity.

Epistemic Challenges and the Necessary Epistemic Responses

Someone alive today may be no smarter than Aristotle or Galen, but there is the presumption that the former knows more than the latter. This presumption—​ which is true, although only superficially so—​in large part comes from the higher degree of apparent confidence, due to science and the scientific method, in what is known. Uncertainty cannot be eliminated, however, as the chapters to follow will make clear. A reduction of uncertainty in one aspect usually meets with an increase in uncertainty in another. If people alive today are no more certain than their ancient predecessors, the hope is that at least they are less confused.

The Importance of Philosophical Analyses The usefulness of philosophy arrives primarily from the fact that philosophers have long asked themselves the following question: Is it possible that a scientist, a physician, a healthcare professional, or someone of similar occupation has become confused? An important process in reducing confusion is to “unpack,” or deconstruct, an argument, claim, or proposition into its elements, which are its assumptions and, more importantly, its presuppositions, in addition to factual and relational claims. The main work of philosophers—​unpacking complicated arguments—​allows specific patterns of argumentation to emerge and be revealed as fallacious or valid. The following argument presents an illustrative example; more in-​depth analyses will take place in other chapters. Deep brain stimulation helps with the symptoms of Parkinson’s disease by inhibiting neuronal activity in a part of the brain known as the globus pallidus interna. The purposeful destruction (surgical ablation or pallidotomy) of the globus pallidus interna also improves the symptoms of Parkinson’s disease. The mechanisms of deep brain stimulation and surgical ablation must therefore be the same. This conclusion is false: Multiple different mechanisms may bring about improvement in Parkinson’s disease. The argument rests on an error in reasoning known as the Fallacy of Pseudotransitivity, which corresponds to the following logical form: If a implies b is true, if c implies b is true, and if b is true, then a must imply c. In this specific case, a stands for deep brain stimulation, b for improved symptoms of Parkinson’s disease, and c for pallidotomy. The value of philosophical analysis is displayed in the unpacking of any scientific or medical argument to reveal its logical structure and, in this case, to see it as the Fallacy of Pseudotransitivity. This is not to say that it is impossible for a to imply c or that it is impossible for pallidotomy and deep brain stimulation to share the same mechanisms (they do not). It is to say simply that claims to either effect lack proof. The important point here is that any argument that can be analyzed and found to reduce to the form of if a implies b is true, if c implies b is true, and if b is true, then a must imply c is an invalid argument, and

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the conclusion is not assured. The chapters that follow will show that although the Fallacy of Pseudotransitivity offers potent means for generating scientific and medical hypotheses, it is not a form of proof. Thus, any medical argument in the form of the Fallacy of Pseudotransitivity must be held with a degree of skepticism and invite further investigation.

Future Areas of Study in Logic Consideration of evolutionary epistemology and logic starts with relatively straightforward propositional and syllogistic deduction and understanding of logical fallacies. These will be explored in greater detail in subsequent chapters and, fortunately, propositional and syllogistic deduction sufficient for the purposes of this text. However, the discipline is far richer. As will be demonstrated, deductive logic can be expanded to partial syllogistic logic, fuzzy logic (Helgason and Jobe, 1999, 2004; Ohayon, 1999), probability, and statistics. Advances in chaos and complexity theory will expand analysis beyond current statistical approached (Anderson et al., 2005). Unpredictability is the hallmark of chaotic and complex systems such that any arbitrarily large set of observations will not converge necessarily on a single solution. Thus, the large numbers theorem and the notion of central tendency central to statistics may not be tenable in chaotic and complex systems discussed in later chapters. Unfortunately, most biological processes likely are chaotic and complex, and so new methods will be needed (Sadegh-​Zadeh, 2012). As discussed in Chapter  5, the scientific method is the Fallacy of Confirming the Consequence, which is of the form if a implies b is true and b is true, then a is true, whereas b can be true for any number of reasons besides a. However, the argument can be salvaged by changing to the form if and only if a implies b is true and b is true therefore a is true. The only way to establish the condition of if and only if a is by exhaustion. There is nothing other than a that could conceivably imply b. New methods for first expanding the number and range of hypotheses to be exhaustive include machine learning (such as genetic algorithms for hypothesis generation) and data mining to generate nonintuitive hypotheses (Oquendo et al., 2012; Sadegh-​Zadeh, 2012).

4

Medical Epistemology THE ISSUES

Case A 45-​year-​old woman asks Physician A and Physician B (both gynecologists) whether she should have a mammogram. She asks because a friend has recently received a breast cancer diagnosis, and yet she has not experienced any symptoms, noticed any changes in her breasts, or had any family members develop the disease. Physician A recommends that the woman have one. Physician B sees no reason for her to have one and indeed argues against one. Which physician has made the correct recommendation? More importantly, why did Physician A  recommend a mammogram while Physician B did not? These are not at all idle or academic questions because quite real implications hang on them. Moreover, most physicians are not of one mind when it comes to venturing answers (Miller et al., 2014). If Physician A is right, Physician B is wrong, and the woman follows the recommendation of Physician B, the woman faces the prospect of living with cancer that has gone undetected. Would it have made a difference? If Physician A is wrong, the woman follows the recommendation of Physician A, the patient faces the risk of unintended consequences of a mammogram that outweigh any benefit of the cancer’s earlier detection. Would a questionable change revealed in a mammogram necessitate a biopsy? Would the site of the biopsy develop an infection? Would an inconclusive initial biopsy necessitate a more invasive investigation? Is it possible that both physicians are right? Is it possible that both physicians are wrong? These questions are not questions of facts because it is likely that both physicians know the data. Rather, these questions center on interpolation or extrapolation from the mutually recognized facts, and they fundamentally are epistemic rather than ontological questions. Neither physician likely will dispute the findings (results) of the radiologist. The contrary opinions of the two physicians, at the very least, suggest uncertainty in the inferences from the

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mammogram as it relates to the subsequent course of action even prior to any data from the mammogram. The decision to perform or not perform a mammogram is a dichotomous conclusion from continuous variables that relate the consequences of having and not having a mammogram. The continuous variable in this case is the radiologist’s estimation of the probability of cancer. The consequence is that the Principle of the Excluded Middle pertains, in a general sense. The principle as used in deductive logic holds that any statement, such as the answer to the question of whether to do a mammogram, can be one value:  do the mammogram or not. Generally, the Principle of the Excluded Middle holds that a premise, such as a logical variable, must be either true or false, but not both true and false or neither true nor false. In this case, true can be doing a mammogram and false could be not doing a mammogram. The Principle of the Excluded Middle holds in some forms of logical induction: all ravens are black, for example. The great value of holding the Principle of the Excluded Middle is that any valid argument based on true premises will result in conclusions that are certain.

Deduction Deduction, or what is perceived as deduction, has a prominent position in medical decision-​making. For example, one exemplar approach to diagnosis is the hypothetico-​deductive approach. This approach can be described as formulating a hypothesis; establishing both positive and negative predictions—​what is and what is not to be found—​respectively; examining whether the predictions hold; and then coming to some conclusion even if the conclusion is to consider other hypotheses in the event that neither the positive nor the negative predictions are demonstrated. The degree to which the hypothetico-​deductive approach is used in medicine, as opposed to the use of pattern recognition, is debated (see Gatens-​Robinson, 1986; Groen and Patel, 1985; McGuire, 1985; Werner, 1995). Perhaps the distinction between the hypothetico-​deductive approach and pattern recognition is more apparent than real (Chapter 19). The hypothetico-​ deductive approach is quite clear when the medical decision-​making is in the manner of propositional logic, which is of the general form if a implies b is true and a is true, then b is true. For example, the presence of test result A implies disease B, test result A is found true, therefore the pa­ tient has disease B. Thus, given true premises, test result A was found, and valid propositions, test result A implies disease B, then the conclusion the patient has disease B must be true. Note that the true conclusion also rests on the proposition that test result A implies disease B is known to be valid. Whether or not the proposition is true is another matter. Unfortunately, as will be discussed later, this generally is not how the argument is structured.

Medical Epistemology: The Issues

The hypothetico-​deductive approach is less clear when the medical decision argument is couched as a syllogistic deduction:  for example, patients with levels of serum porcelain above a certain threshold have the syndrome of hyperporcelainema, patient X has levels of serum porcelain above threshold, therefore patient X has the syndrome of hyperporcelainema. This syllogistic deduction can be reconstructed to a propositional logical form of if patient X has a serum porcelain level above threshold, then the patient has the syndrome of hyperporcelainema, the patient has porcelain levels above threshold, therefore the patient has the syndrome of hyperporcelainema. The power of deduction comes from the certainty vouchsafed to a conclusion by the validity of an argument (chain of reasoning or propositions) and the truth of its premises (knowledge claims).

The Limits of Deduction Consider Argument 4.1 that follows. Assuming the truth of the premises and the validity of the propositions, the conclusion that John Doe will die of cancer is inescapable. Argument 4.1 Major premise: All tobacco smokers die of cancer or heart disease. Minor premise: John Doe smokes tobacco. Conclusion: John Doe will die of cancer or heart disease. Yet there is reason to doubt the major premise as some smokers may die of other causes. Smoking tobacco is a matter of degrees; that is, a range of more than two values of smoking. For example, the degree of “smoking tobacco” may be based on the frequency of smoking. A so-​called heavy smoker will more likely die of cancer or heart disease. In this case, the logical argument “a implies b” (smoking implies an increased risk of dying of cancer and of heart disease) is not dichotomous (true or false) but continuous (admitting of degrees). Now to be determined is the degree to which the risk of dying of cancer or of heart disease is relative to the frequency with which John Doe indulges his habit of smoking tobacco. This changes the nature of the syllogistic deduction, as seen in Argument 4.2. The major premise in Argument 4.2 goes from tobacco smokers face a greatly increased chance of dying of cancer and heart disease to some tobacco smokers will die of cancer or heart disease. Now it becomes unclear whether John Doe is among those smokers who will get cancer or heart disease or among those smokers who will not get cancer or heart disease (Argument 4.3). Argument 4.1 becomes a partial syllogism. This is completely unhelpful when treating John Doe and attempting to get him to stop smoking.

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Argument 4.2 Major premise: Tobacco smokers face a greatly increased chance of dying of cancer and heart disease. Minor premise: John Doe smokes tobacco. Conclusion: John Doe will die of cancer or heart disease. Argument 4.3 Major premise: Some tobacco smokers will die of cancer or heart disease. Minor premise: John Doe smokes tobacco. Conclusion: John Doe will die of cancer or heart disease. One could recast that the partial syllogism to probabilities based on the degree of tobacco smoking does not change the logical structure. The logical argument then becomes proportional to the degree a person smokes tobacco, the patient is at an increased risk of dying of cancer and heart disease; John Doe smokes tobacco to a certain degree; thus, to the degree John Doe smokes tobacco, John Doe is at a proportional risk of dying of cancer and heart disease. The Principle of the Excluded Middle loosens the arguments by using probabilities and statistics, but these probability syllogisms are closer to valid forms of arguments in propositional deductive logic than is the use of “some” in the partial syllogism. Statements with higher (or lower) probabilities with a high statistical confidence are more likely to be true by approximating the Principle of the Excluded Middle. Similarly, arguments based on probability and statistics further from valid logical arguments (that is, in the form of a logical fallacy) are more likely to be false. (The relationship of probability to logic is discussed in detail in Chapter 5 and Appendix B, with the latter available at www.oup.com/ us/medicalreasoning).

The Patient Imperative The scientific issue of John Doe’s risk of dying of cancer or of heart disease may come to rest at a qualitative yet continuous measure, which may perhaps be the most exact answer. It is the most scientific answer. Yet how does it relate to the medical question of whether John Doe should cease his tobacco use? From the standpoint of a risk to cost–​benefit analysis, it appears evident on its face that indeed he should quit. This conclusion, however, rests on the presupposition that smoking tobacco benefits neither John Doe nor society. Even if an outcome variable is continuous, producing an outcome that is some range of values (e.g., probabilities), medical necessity demands its dichotomization. Dichotomous decisions typically conceal a threshold that results in a “yes” when crossed in one direction and results in a “no” when crossed in another direction. Identifying this threshold is less a scientific matter than it is a social, moral, ethical, legal, and political matter. The once

Medical Epistemology: The Issues

controversial practice of adding fluoride to drinking water, for example, is now deemed as ordinary as adding iodine to table salt and vitamin D to milk as the costs are considered minimal and thus the barrier to use likewise is minimal (Montgomery and Turkstra, 2003).

Dichotomization Based on Statistical Significance In science, as in medicine, experiments are arguments constructed of premises and propositions cast in scientific form. They produce results that are dichotomized under two category headings—​statistically significant and not statistically significant—​according to consideration of their p value, for example, which is the probability that any detected difference or result in an experiment is due to chance. In research, for example, a typical threshold for dichotomization is reached with a p value of less than 0.05, which indicates that a result has less than a 5% probability of being a fluke just due to chance. However, the exact threshold for statistical significance depends on scientific meaningfulness. In the experiment to detect a Higgs boson, the threshold was 5 sigma, or five times the standard deviation (a measure of variability) in the detection measures. For the Higgs boson, the probability of the detection being a fluke just due to chance was (100–​99.99994%). In the past, the use of 3 sigma was found to lead to an error. Scientific dichotomization serves the purpose of inspiring confidence in a claim’s reliability but is internal within the experiment. In the past, clinical research similarly used statistical significance for confidence in the medical scientific claim. For example, it bears on whether a treatment changes some measure of disease. However, this has little to do with approaches to treating a disease. Doubtless anyone suffering a horribly painful and disabling disease would pursue a treatment that produces no side effects and costs nothing, even if a clinical trial of it had a p value of slightly greater than 0.05. To equate scientific statistical significance with clinical meaningfulness is to fall prey to the logical fallacy called the category error: using a term or concept in one situation in another situation in which it is not appropriate. Certainly, holding that the hypothetico-​ deductive approach in medical decisions as deductions certainly conveys the certainty of deduction, even though it is an error—hypothetico-deduction is not deduction. The hypothetico-​deductive approach as used in medicine actually is the Fallacy of Confirming the Consequence, but it is a very useful and important fallacy if used properly. Logical reasons for the failure of deduction and the importance of logical fallacies are discussed elsewhere (see Chapters 4 and 5). Indeed, it will be demonstrated that many manners of reasoning that one would not ascribe to logical deduction but rather, by default, to practical reasoning will be seen as manifestations of the productive use of logical fallacies. Again, this will be covered in detail elsewhere.

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As will be demonstrated, the limitations of deduction in medical decision-​ making relate to the linking verbs between the premises. This is most evident in syllogistic deduction and can be seen by contrast to Aristotle’s practical syllogisms. Consider the following two syllogisms: Argument 4.4 Major premise: All humans are mortal. Minor premise: Socrates is a human. Conclusion: Socrates is mortal. Argument 4.5 Major premise: When humans are hungry, they eat. Minor premise: I am hungry. Conclusion: I should eat. As can be seen from Argument 4.4, that Socrates is mortal follows as a necessity. It cannot be any other way (see Appendix A for an introduction to logic, available at www.oup.com/us/medicalreasoning). “Socrates” is a subset, which is wholly contained within the set of humans. “Humans” is a subset that is wholly contained within the set of things mortal. Thus, Socrates also has to be in the set of all things mortal. The conclusion in Argument 4.5, which is an example of a practical syllogism, does not necessarily follow from the premises. One could be hungry but not eat. One reason for the lack of force or certainty is that humans that are hungry is not a subset wholly contained within the set of things that eat. While I am hungry places me as a subset wholly contained within the set of humans that are hungry, it does not follow that I am hungry is wholly contained within the things that eat. I could refuse to eat. The verbs in Argument 4.4 linking the major, minor, and bridging terms are state-​of-​being verbs such as “is” and “are.” In Argument 4.5, the linking verb in the conclusion is “should” and does not indicate a state of being. It is an action verb suggesting intended action and, most importantly, suggesting causation rather than state of being. State-​of-​being verbs indicate memberships in different sets. Action or intention verbs do not imply the necessity of membership is a specific set. The intentions need not be carried out. Argument 4.5 looks seductively like a valid syllogism, but it is not. Therein lies the potential for logical and epistemic mischief, as will be seen.

What Physician A and What Physician B Thought Physician A may have thought detecting breast cancer at an early stage of development via a mammogram would be easier to treat. Removing all the cancer

Medical Epistemology: The Issues

will be more assured when it is confined to a small area in the breast. The causal reasoning seems very intuitive but needs declaration:  the presence of cancer cells causes metastatic cancer and metastatic cancer causes death. It would seem definitional, approximating, and analytic truth in philosophy. The notion that early detection is critical to beating the disease just makes intuitive sense. Physician B appears to be of the mind that there is simply no data to support the use of mammography in a 45-​year-​old patient who shows no symptoms or risk factors. The US Preventive Services Task Force has stated that there is little evidence of efficacy of mammography for women younger than 50 years of age (http://​www.uspreventiveservicestaskforce.org/​uspstf09/​breastcancer/​brcanrs. htm):  “Screening between the ages of 50 and 69  years produced a projected 17% (range, 15% to 23%) reduction in mortality (compared with no screening), whereas extending the age range produced only minor improvements (additional 3% reduction from starting at age 40 years and 7% from extending to age 79 years)” (Mandelblatt et al., 2009). This statement has met with disagreement (Feig, 2014). The study just described is epidemiological in nature, without invocation or attribution of causal mechanisms. The question is whether the set of women undergoing mammography under age 50 years is (state-​of-​being verb) the same set as those undergoing mammography between the ages of 50 and 69 years of age with respect to risk of death. If these two groups are in the same set, meaning they have the same risk of death, then there is no incremental benefit for performing mammograms prior to age 50. The syllogistic argument becomes: Argument 4.6 Major premise: Screening before age 50 produces no incremental benefit. Minor premise: My patient is younger than 50. Conclusion: My patient is not benefited incrementally. Assuming that the premises are true and the argument is valid, then, perforce, the conclusion will be true. The argument offered by Physician A is a practical syllogism that requires notions of causality and is fundamentally weaker from a logical perspective. On that basis, the 45-​year-​old patient should not undergo a mammogram (unless the mammogram had no cost or risks, but this will be expounded upon later). There are a number of grounds by which to question Argument 4.6. The demonstration that screening before age 50 does not produce any incremental benefit is an empirical issue. Assuming proper methodologies, it would be hard to refute the premises. However, there are grounds to invalidate such syllogisms, which will figure prominently in the application of evidence-​based medicine—​the form held synonymous with randomized controlled trials. If Argument 4.6 can be shown to be the Fallacy of Four Terms, then the conclusion is invalid.

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Typically, a valid syllogistic deduction has three terms: major, minor, and bridging. The two premises combine the bridging term with the major and minor terms. The structure is shown here as: Argument 4.7 Major Premise: Bridging term is major term. Minor Premise: Minor term is bridging term. Conclusion: Minor term is major term. One form of the Fallacy of Four Terms occurs when there are two versions of the bridging term for a total of four terms, which would have the following structure: Argument 4.8 Major Premise: Bridging term 1 is major term. Minor Premise: Minor term is bridging term 2. Conclusion: Minor term is major term. If bridging term 1 and bridging term 2 are identical, then the syllogism is valid. If, however, they are not identical, then the syllogism is invalid and demonstrates the Fallacy of Four Terms. In Argument 4.6, the bridging term is screening under age 50. If there are two different forms of screening under age 50, then the conclusion is invalid. Perhaps my patient aged 45 is just somehow different from the group screened under age 50. If my patient carries the BRCA1 gene or has a strong family history of breast or ovarian cancer, then my patient indeed may be different from the group screened under age 50, and the conclusion, being invalid, would lose force. The clinician advising the patient under age 50 likely would take into consideration other factors that contribute to the risk of breast cancer. The need to do so can rightfully be an example of practical reasoning, but, as can be seen, whether the clinician realizes it or not, the clinician is taking into consideration the possibility of the Fallacy of Four Terms. Just because the clinician does not formally express concern for the Fallacy of Four Terms does not mean that the clinician’s practical reasoning is somehow of a different category than logic. As will be seen, the Fallacy of Four Terms becomes a type of metaphor. Epistemic risk, composed of epistemic distance and epistemic degrees of freedom, is a tool to assess the utility of metaphors, which are discussed in detail later.

Comparing Apples and Oranges A curiosity emerges from the aforementioned data regarding mammography based on age. There is approximately a 3% greater reduction in mortality with mammograms ordered for patients aged 40–​50 years compared to mammograms

Medical Epistemology: The Issues

for patients aged 50–​70 years. Yet the recommendation nevertheless is to refrain from ordering mammograms in patients younger than 50  years of age. This curiosity is explained by noting that although mammograms for women aged 40–​50 years reduce mortality, the incidence of breast cancer in this age group is lower than it is in the age group of 50–​70 years. Thus, according to Bayes’ theorem, the false-​positive rate will be much higher in the group of patients aged 40–​50  years, leading to many more invasive procedures, such as biopsy, and their attendant complications. The issue of mammograms in patients under 50 years of age is only partially an issue of a reduction in mortality associated with the diagnosis of cancer. It is also an issue of the morbidity associated with a false positive and the consequent risk to which the patient is subjected. How, then, to balance a possible 3% reduction (assuming it is real) versus the adverse consequences of a false positive? There appears to be no simple calculus by which to make such a comparison. This would seem to affect a schism between logic and the necessary balancing of two incommensurate issues. Clearly, humans do make such comparisons and the occasions can be subject to logical analysis, although, in this case, initially induction. What is required is some common currency. One potential currency is what some group of humans is willing to pay to gain benefit or avoid adverse effects. One such approach is to take therapies long considered acceptable, such as a coronary artery bypass for unstable angina. The cost of providing the therapy can be calculated. The benefit can be measured in terms of improvement in quality of life per year (QALY). Thus, the cost per unit quality of life years can be calculated as a QALY (Hirth et al., 2000). The QALY for a coronary artery bypass then becomes the benchmark for weighing benefit, risk, and cost for other treatments. This analysis over a range of therapies considered effective by consensus allows an induction to general principles that, in effect, allows comparisons between apples and oranges. One needs not default to some nonlogical practical reasoning. Applied to the case of mammograms, the cost of the number of patients under the age of 50 needed in order to detect and cure one case of cancer, as well as the cost of treatment, can be determined. Likewise, the cost incurred by pursuing false positives, including treatment of complications, can be determined. One then can determine the resulting ratio and compare that ratio to what has been traditionally considered acceptable. Whether or not society would be happy with the result and its consequences is another matter. After all, in 1988, the United States spent millions of dollars attempting to free four whales trapped in an ice field (https://​en.wikipedia.org/​wiki/​Operation_​ Breakthrough). Nonetheless, such induction and subsequent deduction are possible. This approach is little different from that used in some constructions of biomedical ethics. Common morality essentially is induced from the predilections of reasonable and moral persons (Beauchamp and Childress, 2013). From

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common morality, ethical principles are derived for the purpose of medical–​ ethical decision-​making as described in Chapter 2.

Facts Are Insufficient The preceding discussion suggests that, at least as far as Physician A and many of her peers are concerned, facts arriving as results of clinical trials provide an insufficient basis for medical decisions. Importantly, in many medical decisions, facts are never enough, especially when they conflict with intuitive notions of pathophysiology (scientific theory). What Thomas Kuhn wrote about the acceptance or refutation of scientific theories in The Structure of Scientific Revolutions (1962) may be said of medicine. On the basis of historical evidence, Kuhn clearly demonstrated that acceptance of scientific theories often had little to do with any supporting facts. Theories are evaluated instead in a context or paradigm, and paradigms often trump facts. The same holds true for medical decision-​making. Allopathic medicine specifically has long been characterized by a mistrust of empirical observations as a sole basis for diagnosis and treatment—​ perhaps the kind employed by Physician B.  Indeed, the American Medical Association’s (AMA) 1847 Code of Ethics, assuming the mantle of scientific medicine, discouraged any associations with “Empirics,” medical practitioners who based their treatments on observational experience alone. Empirics included herbalists and medical practitioners who prescribed steam baths (steam doctors). Herbalists managed to win some respect, at least from the public and some politicians. Samuel Hahnemann (1755–​1843), founder of homeopathy, wrote “I have not thought it beneath me to converse with Root and Indian doctors, and everyone who has professed to possess any valuable remedy, or any improved method of treating any disease” (Coulter, 1982). In 1847, the AMA eschewed any such egalitarianism, forbidding association and encouraging those in power to suppress these alternative forms of medicine, until the US Supreme Court ruled that such a position is in violation of antitrust laws as it relates to chiropractic (Wilk v. American Medical Association, 895 F.2d 352 [7th Cir. 1990]). Interestingly, Justice Getzendanner wrote: The plaintiffs clearly want more from the court. They want a judicial pronouncement that chiropractic is a valid, efficacious, even scientific health care service. I  believe that the answer to that question can only be provided by a well-​designed, controlled, scientific study.  .  .  . No such study has ever been done. In the absence of such a study, the court is left to decide the issue on the basis of largely anecdotal evidence. I  decline to pronounce chiropractic valid or invalid on anecdotal evidence.  .  .  . The defendants [American Medical Association] have offered some evidence as

Medical Epistemology: The Issues

to the unscientific nature of chiropractic. The study of how the five original named plaintiffs diagnosed and actually treated patients with common symptoms was particularly impressive. This study demonstrated that the plaintiffs do not use common methods in treating common symptoms and that the treatment of patients appears to be undertaken on an ad hoc rather than on a scientific basis. And there was evidence of the use of cranial adjustments to cure cerebral palsy and other equally alarming practices by some chiropractors [emphasis added]. Arguably, the notion of a scientific basis reflects a presupposition of causal mechanisms, more akin to the reasoning mechanisms of Physician A.

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Deduction, Induction, and Abduction THE BASICS

Case The patient has strep throat, which is a bacterial infection. Antibiotics will clear it up, so she must start on them right away. NURSE: How do you know the patient’s illness isn’t viral? If it is, antibiotics won’t help. PHYSICIAN: The patient has the symptoms of pain in her throat. Her throat’s red and covered with exudate (pus). Plus, she has swollen lymph glands in her neck and a fever. She has strep throat. NURSE: Shouldn’t we get a throat culture, just so we can rule out a viral cause? PHYSICIAN: A culture is unnecessary. I’ve seen cases like these countless times, and every time it turns out to be strep throat. Having the patient start antibiotics now rather than waiting for the results of a culture will save time—​and money. PHYSICIAN:

The seemingly straightforward nature of this case belies a conundrum. Excessive prescription of antibiotics is understandably a concern. It besets patients whose viral pharyngitis (throat infection) is mistaken for strep (bacterial) throat with unnecessary expense and risk of complications, and it contributes to the increase of antibiotic-​resistant strains of infectious bacteria. By the same token, scrupulous caution should not lead to delays and expense from ordering unnecessary throat cultures or other tests. Should a throat culture be ordered? Should antibiotics be prescribed pending verification of a streptococcal infection? How best to treat the patient may have no clear, simple answer, but the premises and propositions, the elements of the question, may be analyzed for their strengths and weaknesses. The analysis offers a basis for a reasonable judgment. The physician in this case makes an initial diagnostic conclusion: “The 56

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patient has strep throat, which is a bacterial infection. Antibiotics will clear it up, so she must start on them right away.” Although it may not be apparent, this conclusion rests on three premises and two propositions, which leads the argument to a conclusion. The premises include this particular patient (premise a, or there is such a person), an entity called strep throat (premise b), and antibiotics (premise c). Two propositions link a to b, that is, a implies b, or that the patient has strep throat, and the second proposition relates premise b to c, or b implies c: strep throat leads to antibiotics. Because the presence of strep throat implies the presence of an illness that is cured by antibiotics, the patient should be treated with antibiotics (conclusion a implies c).

Logical Argumentation The argument just given, which is deductive, corresponds to the Principle of Transitivity of the following logical form: If a implies b is true, b implies c is true, and a and b are true, then a must imply c. In this case, a is “this particular patient,” b is “strep throat,” and c is “antibiotics.” Thus, if the patient implies strep throat and strep throat implies antibiotics are true, then the conclusion the patient should be treated with antibiotics is also true. It is the nature of deduction that true premises, such as a, b, and c, and valid propositions, such as a implies b and b implies c, invariably lead to true conclusions. The task becomes determining whether the argument’s propositions, a implies b and b implies c, are valid. The nurse questions the validity of the proposition a implies b (this particular patient has strep throat). Were the patient to have an illness other than strep throat, the proposition a implies b would be invalidated because b is false, which necessarily means that a implies b is false. The conclusion that the patient requires antibiotics, therefore, cannot be accepted as invariably true. This is not to say that the patient should not be treated. It is to say, rather, that no deductive logic supports the decision to treat with antibiotics. In answer to the nurse’s question, the physician says: “The patient has the appropriate symptoms. Her throat’s red and covered with exudate (pus). Plus, she has swollen lymph glands and a fever. She has strep throat.” The diagnosis of strep throat from symptoms seems to follow from a deduction from which it borrows its sense of certainty. The argument has the form of if the patient had strep throat then the patient would have pain in the throat, fever, enlarged lymph nodes, and an inflamed throat with exudate. The argument is of the form if a implies b is true and b is true then a must be true where a is strep throat and b is the combination of pain in the throat, fever, enlarged lymph nodes, and an inflamed throat with exudate. But the argument is not valid deduction and consequently does not have the certainty of deduction. The argument is the Fallacy of Confirming the Consequence, otherwise referred to less pejoratively

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as abduction. Abduction is arguably common, if not fundamental, to the practice of medicine. Yet such decision-​making fundamentally relies on a fallacy. The critical argument is if the patient had strep throat then the patient would have pain in the throat, fever, enlarged lymph nodes, and an inflamed throat with exudate. Should the symptoms and signs be demonstrated, the disease—​having strep throat—​is true. The lack of validity is demonstrated by the fact that the patient may have diphtheria: in other words, rather than strep throat. The ultimate response to the nurse’s questioning the validity of the premise, that this particular patient has strep throat, is countered by a different kind of response by the physician who states “I’ve seen cases like this countless times, and every time it turns out to be strep throat.” The assertion that the patient has strep throat by the physician is not deductive in nature at this point; it is inductive. The problems of making such an inductive claim are reviewed later. In recommending a throat culture, the nurse gets to the heart of the Fallacy of Confirming the Consequence. The nurse appears to believe that the presence of a positive throat culture, in addition to the other symptoms and signs, is more supportive of the conclusion. In other words, the throat culture would be positive and all the other symptoms and signs present only if the patient has strep throat. All other causes would be excluded. To accommodate the nurse’s recommendation requires that the reasoning be modified to correspond to the following form: If and only if a (strep throat) implies ([symptoms and signs] is true h [positive throat culture] is true) and ([symptoms and signs] is true and h is true), then a is true. The argument constructed using if and only if converts the fallacy to a valid deductive argument of the modus ponens type as if ([symptoms and signs] is true and if h [positive throat culture] is true) implies b (strep throat) is true, and ([symptoms and signs] is true and if h) is true, then b is true; assuming for the moment that the proposition if ([symptoms and signs] is true and if h [positive throat culture] is true) implies b (strep throat) is true and there may be reason to doubt. The physician’s ultimate defense of his decision is “a culture is unnecessary. I’ve seen cases like these countless times, and every time it turns out to be strep throat.” The physician, in other words, attempts to validate his argument by induction, a mode of reasoning that moves from a set of specific observations to a general claim. If, for example, every raven ever observed is black, then it may be concluded that all ravens are black. Induction subsequently lends itself to a generalized deduction. If, for example, all ravens are black, and if a specific bird is a raven, then it may be concluded that the specific bird is black. Similarly, if every patient ever seen by the physician that has had enlarged lymph nodes, a fever, and a sore, inflamed throat covered with exudate was found to have a strep throat, then the physician may induce that any patient who has enlarged lymph nodes, a fever, and a sore, inflamed throat covered with exudate has strep throat.

Deduction, Induction, and Abduction

The case may simply be that all patients encountered by the physician in the past or in the future who have these symptoms and signs will have had strep throat. If it is indeed the case, then treating those patients with antibiotics is entirely appropriate and ordering a throat culture is a waste of time and money. Note that, in this case, well-​established algorithms such as the Centor rules may not have sufficient positive and negative predictive value or would be inappropriate (Fine et  al., 2012). It remains unknown, however, whether, somewhere in the community served by the physician, a patient exists whose symptoms and signs are due to an unusual bacterial (not sensitive to the antibiotic typically chosen by the physician), viral, or fungal infection rather than strep throat. In treatment of such a patient, application of inductive reasoning leads to a needless use of antibiotics prescribed for strep throat. Just as the discovery of a single raven of some color other than black invalidates the claim that all ravens are black, the discovery of a patient whose symptoms, although identical to those of strep throat, are due to another illness invalidates the claim that patients with such symptoms always have strep throat. If one imagines that of 1,000 patients presenting with enlarged lymph nodes, fever, and a sore, inflamed, exudate-​covered throat, only 1 patient has an illness other than strep throat, and if one assumes that the treating physician’s past experience is predictive of her future experience, then 1 of every 1,000 such patients in her care will be inappropriately given antibiotics specific to Streptococcus. The physician may reduce the risk of misdiagnosis in this case by obtaining a throat culture from every patient, which would entail wasting resources on 999 of every 1,000 patients. An abductive approach to administering antibiotics fails in light of the possibility that some illness other than strep throat affects 1 of every 1,000 patients with the same symptoms and signs as the 999 others with strep throat. The dichotomous nature of the necessary decision, following from the Principle of the Excluded Middle, holds that, for the physician’s decision, the patient either has or does not have strep throat. As will be seen, the partial syllogism, leading to probability and statistics, offers one means to deal with the conundrum.

Consequence of the Failure to Uphold the Principle of the Excluded Middle The situation just described demonstrates the consequence occurring when the Principle of the Excluded Middle does not hold. Even 1 patient out of 1,000 not having strep throat after meeting the physician’s criteria sows doubt that affects every diagnosis made by the physician. This can be appreciated by reformatting the propositional deduction into a syllogistic deduction.

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Argument 5.1 Major premise:  Patients with red, inflamed throat covered with exudate, fever, and enlarged lymph nodes have strep throat. Minor premise: My patient has red, inflamed throat covered with exudate, fever, and enlarged lymph nodes. Conclusion: My patient has strep throat. The syllogism is false because the major premise is not true. One patient out of 1,000 has the symptoms and signs but does not have strep throat. Clinical judgment would then reframe the syllogism to: Argument 5.2 Major premise: Some patients with red, inflamed throat covered with exudate, fever, and enlarged lymph nodes have strep throat. Minor premise: My patient has red, inflamed throat covered with exudate, fever, and enlarged lymph nodes. Conclusion: My patient may have strep throat. The reformatted syllogism becomes a partial syllogism, which, while truer, is unhelpful. Is my patient among those patients with red, inflamed throat covered with exudate, fever, and enlarged lymph nodes have strep throat who have strep throat or among those that do not? What can be said of practical reasoning is that it attempts to quantify the partial syllogism to something like: Argument 5.3 Major premise: X% patients with red, inflamed throat covered with exudate, fever, and enlarged lymph nodes have strep throat. Minor premise: My patient has red, inflamed throat covered with exudate, fever, and enlarged lymph nodes. Conclusion: My patient has X% chance of having strep throat. This form of the partial syllogism is called the probability syllogism. Astute clinicians are good at either knowing or estimating the probability, X%. Nevertheless, these decisions are structurally logical in nature or appeal, in the manner of a deduction, but are limited. In a situation in which 500 of 1,000 patients presenting with symptoms and signs of strep throat but caused by something other than Streptococcus pneumoniae, throat cultures may be critical. Note that the probability of a patient in the population of concern is called the prior probability of a patient actually having strep throat, which in this case is 0.5 (500 of 1,000). The task thus becomes determining, prior to obtaining a throat culture, the cutoff in the probability that a patient who presents with symptoms and signs associated with strep throat has an illness other than strep throat. Probability has been introduced to rescue the partial syllogism, to provide a greater utility while

Deduction, Induction, and Abduction

preserving some sense of certainty. The effect is to relax the rigor imposed by the Principle of the Excluded Middle. As described previously, the prior probabilities of strep throat are critical in decision-​making. But estimating probabilities is difficult. For example, in one sample of 1,000 patients with the appropriate symptoms and signs, 400 patients may be found to have strep throat, for a risk of 40% establishing the prior probability at 40% (0.4). In another sample of 1,000 subjects, 200 similar patients are found to have strep throat. Thus, what is the actual probability, 40% or 20%? The role of statistics is to give some degree of confidence regarding the true probability. Indeed, the central tendency of probabilities calculated over a number of samples and the confidence interval around them can be determined (see Appendix B, available at www.oup.com/us/medicalreasoning).

The Problematic Nature of the Throat Culture The dilemma appears to be whether the patient requires a throat culture. The presumption is that a true positive throat culture (a) implies a definitive diag­ nosis of strep throat (b). This presumption corresponds to the following logical form: If a implies b is true and a is true then b must be true (modus ponens). When this argument is extrapolated to a partial syllogism, which is further extrapolated to a probability syllogism, the result establishes the sensitivity of the test. When combined with the prior probability, the positive predictive value of the throat culture can be established (see Appendix B, available at www.oup. com/us/medicalreasoning for further discussion of the probability). Similarly, there is the presumption that a negative throat culture demonstrates an absence of Streptococcus (¬a [not a] or a negative throat culture) means ¬b (not b or no strep throat). The argument could be constructed as if a strep throat implies a positive throat culture is true and a positive throat culture is false (a negative throat culture), then a strep throat must be false (no strep throat). This form is called modus tollens and is of the general form if a implies b is trued and ¬b is true, then a must not be true (¬a is true). The notion of a positive throat culture solely and necessarily means strep throat and a negative throat culture solely and necessarily means the absence of strep throat and thus demonstrates the Principle of the Excluded Middle at work. Just as the modus ponens argument for a positive diagnosis can be extrapolated to a positive predictive value, so can the modus tollens argument be extrapolated to a negative predictive value. It is highly unlikely that any test is 100% accurate. However, the word “accurate,” as commonly used, is insufficient. Accuracy indicates specificity and sensitivity. If it is assumed that the specificity and sensitivity of the throat culture test are 90%, and the test is applied to 1,000 patients presenting with appropriate symptoms and signs of strep throat, of whom 999 actually have strep

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throat, then the 1 patient who has an illness other than strep throat faces a high probability of having a negative throat culture (true negative). The fact that 999 out of 1,000 cases actually have strep throat translates to a prior probability of 999/​1,000, or 0.999. Also, of the 999 patients who have strep throat, 9 (0.9%) will have a negative throat culture (false negative). Were the physician treating these 1,000 patients to diagnose all of them with strep throat immediately upon examining their symptoms without a throat culture, more (9) of them would receive antibiotics than if the physician awaited the results of throat cultures. If the 1 patient with an illness other than strep throat experienced no ill effects from taking antibiotics, the physician’s practice was better for having foregone throat cultures. Had the physician used the results of the throat culture solely to decide about prescribing antibiotics, 9 patients with strep throat would not receive the appropriate antibiotic care. The situation would be quite different were the number of patients with and without strep throat divided at 500 each as this would translate to a prior probability of 0.5. Prior probabilities are extremely important in medical decisions and can be understood in the formalization resulting in Bayes’ theorem, to be discussed in detail in Chapter 4 and in Appendix B at www.oup.com/us/medicalreasoning. Failure to appreciate this fact is one source of the increasing animosity felt by general practice physicians toward specialists. This animosity has prompted some general practice physicians to defend their practices against specialists, whom they consider prone to ordering excessive tests. The respective approaches taken by general physicians and by neurologists to treating headaches offer a useful example. General physicians tend to forego magnetic resonance imaging (MRI) scans absent any evidence of neurological abnormalities. This approach is reasonable as the vast majority of patients presenting to a general physician with headache have no structural abnormality for an MRI scan to detect. In other words, the prior probability of a patient having such a meaningful abnormality is exceedingly low. However, it is not rare for an MRI to demonstrate an abnormality that is not meaningful but that would be considered a false positive. Scans that show incidental meningioma or other abnormalities, moreover, have a high probability of being irrelevant to the patient’s headache (false positives). Patients usually referred to neurologists, however, are quite different, if for no other reason than that their very referral suggests that they had been distinguished from other patients not referred. The prior probability of a significant structural abnormality confronted by neurologists is consequently much higher. Ordering MRI scans is therefore entirely appropriate given the higher prior probabilities created by winnowing out through the referral process. The preceding discussion helps resolve the conflict often occurring between general practitioners and specialists—​whether or not specialists overly request testing or general physicians do not request enough is a false issue. The question depends on the prior probability of a relevant positive test (as well as the

Deduction, Induction, and Abduction

sensitivity and specificity of the test). When the prior probability is low, then actually performing the test could be detrimental because of false positives and the ensuing investigations and treatments (Chapter 3). When the prior probability is high, the true-​positive rate reasonably exceeds the false-​positive rate, thereby justifying the testing. Thus, the debate should not hinge on whether the general physician or specialist is ordering the test but rather on what prior probabilities each confronts (Chapter 4).

Sensitivity, Specificity, and Positive and Negative Predictive Values as Logical Forms The most important measures are the positive and negative predictive values that take into account specificity and sensitivity, as well as the prior probabilities discussed previously. While perhaps counterintuitive, sensitivity and specificity are relatively useless when assessing a patient at risk for disease A. Using sensitivity to determine whether a patient has disease A is an example of the Fallacy of Four Terms. Consider the syllogism in Argument 5.4. Argument 5.4 Major premise: Ninety percent of all patients with disease A (bridging term) have an abnormal result in test B (major term). Minor premise: My patient may or may not have (minor term) disease A (bridging term). Conclusion: My patient should have test B. In Argument 5.4, the bridging term in the major premise is not the same as the bridging term in the minor premise. Note that the bridging term in the major premise is the sensitivity of test B. The bridging term in the major premise refers to only patients with disease A. The bridging term in the minor premise is a patient who may or may not have disease A. Thus, Argument 5.4 is the Fallacy of Four Terms, demonstrating the lack of utility of the sensitivity. The same argument can be constructed for specificity, as shown in Argument 5.5: Argument 5.5 Major premise: Ninety percent of all patients without disease A (bridging term) have a normal result in test B (major term). Minor premise: My patient may or may not have (minor term) disease A (bridging term). Conclusion: My patient should have test B. Note that the bridging term in the major premise is the specificity of test B. Argument 5.4 can be modified, as shown in Argument 5.6, to include the prior probabilities:

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Argument 5.6 Major premise: Ninety percent of all patients with disease A (bridging term) have an abnormal result in test B (major term). Minor premise: My patient (minor term) has a 10% probability of having disease A (bridging term). Conclusion: My patient has a 9% probability of having a positive test B. The bridging term in the major premise relates to just 100% of patients having disease A and represents the sensitivity of test B. The bridging term in the minor premise is not exactly the same but it is not completely different. Rather, the probability in the minor premise relates to having disease A, which qualifies the relationship between the bridging terms in the major and minor premises. Combining the sensitivity of the test (the percentage of patients with disease A who have an abnormal or positive test B) with the actual risk of having dis­ ease A allows one to determine the positive predictive value of the test, 9% (0.09) for my patient. Whether or not my patient should have test B depends on the consequences of false-​positive and false-​negative test results. Next consider Argument 5.7, related to the specificity of test B: Argument 5.7 Major premise: Ninety percent of all patients without disease A (bridging term) have a normal result in test B (major term). Minor premise:  My patient (minor term) has a 90% probability of not having disease A (bridging term). Conclusion: My patient has an 81% probability of having a negative test B. In both cases, test B underestimates the probability of disease and the probability of not having the disease. The underestimation of the probability of dis­ ease of 1% may not be significant, but the underestimation of the probability of not having the disease of 9% may be. In medical decision-​making, the probabilities must be dichotomized into a yes-​or-​no decision. The situation described in Arguments 5.6 and 5.7 can be reframed because the clinician does not know whether the patient does or does not have disease A, only that the patient is suspected of having disease A. The clinician does know that out of 100 patients suspected of having disease A, 90 will have the disease and 10 will not. The sensitivity of test B means that 81 out of the 90 will test positive (true positives) and 9 will test normal (false negatives). Based on the specificity of test B, of the 10 without the disease, 9 will test normal (true negatives) and 1 will test abnormal (false positive). This means that when the test is applied to the 100 patients suspected of disease A there will be as many false negatives as there are true negatives. The negative predictive value is the ratio of true negatives to total negatives (true and false negatives) and is 50%. The positive predictive value is 91%.

Deduction, Induction, and Abduction

The positive and negative predictive values do not determine whether test B should be done. Rather, the decision is based on the total costs, in the widest connotation, of treating a false negative versus not treating a true positive. If the costs treating a false negative are minuscule compared to the costs of not treating a true positive, then test B should be applied. One can readily appreciate that the conclusions and subsequent medical decisions would be quite different if the prior probabilities in minor terms in both Arguments 5.6 and 5.7 were different, just as the incidence of nonstreptococcal sore throats in the different communities described earlier. Similarly, the conclusions and subsequent actions would be quite different with differing sensitivities and specificities given in the major premises of Arguments 5.6 and 5.7. Note that the nurse did not seem to consider, at least explicitly, the specificity and sensitivity of the throat culture.

Deduction First, an aside to demonstrate the power of deduction: consider the most powerful computer, the most powerful computer software, and the remarkable things that can be accomplished. Now realize that all of that power with all those remarkable capabilities is accomplished by deduction. Every process is the consequence of Boolean algebra, which follows directly from deductive logic. Indeed, the Principle of the Excluded Middle, which is central to deduction and is described more fully later, is implemented in the very binary code that underlies computer operations. The physician’s argument—​“because the presence of strep throat implies the presence of an illness that is cured by antibiotics, the patient should be treated with antibiotics”—​is a form of deduction, or reasoning from a general principle to a specific instantiation. A general principle is some property that is assumed or determined to be true of some set of entities. A  specific instance is then a member of that set of entities (see Appendix A, available at www.oup.com/us/ medicalreasoning). In a syllogistic deduction, the general principle is the major term and the specific instance generally is the minor term. Socrates’s death by poisoning, for example, represents an empirical demonstration of the conclusion “Socrates is mortal,” its major premise “All men are mortal,” and its minor premise “Socrates is a man.” Those things mortal is the general term, and Socrates is the specific term. In propositional deduction, of the form if a implies b is true and a is true then b is true, a is the general term or set of entities, while b is the specific term and b is made to be within the general term or set denoted by a. Logical variables such as a and b may be combined with such operators as implies, which are similar to mathematical operators and carry out logical functions in propositional logic. Variables a and b may be combined with the

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operator AND, which is represented symbolically as ˄. The combination is written as a ˄ b. The expression accommodates only certain values. For a ˄ b to be true, both a and b have to be true; the falsity of either falsifies the expression. Such operations are of great help, as may be seen in the example of a patient who has enlarged lymph nodes, fever, and a sore, inflamed, exudate-​ covered throat. The treating physician was quite willing to let that combination of symptoms imply or entail the conclusion that the patient had strep throat. The physician reasoned that a implies b is true, a is true, and therefore b is true unequivocally (a = the presence of symptoms and signs typical of a sore throat; b = strep throat). Skeptical because she thought that a may possibly have other causes, the nurse, however, suggested that the physician include results of a throat culture (h) in any diagnostic reasoning. Inclusion of h modifies the expression to (a AND h implies b). If a AND h are true, then b is true. The event of a negative throat culture, expressed as ¬h or “not h,” permits an approach to reasoning that corresponds to modus tollens, which establishes b as false because h in a and h is false, and therefore a and h must be false. Although the inclusion of h serves to strengthen the argument, it does not resolve the issue of whether the throat culture indicated by h was medically necessary or helpful, as discussed earlier.

Fallacy of Confirming the Consequence While deduction is a powerful tool providing great certainty, it is rarely used. Instead, something like deduction is used. Consider the deduction in Argument 5.8: Argument 5.8 The presence of enlarged lymph nodes, fever, and sore, inflamed exudate-​ covered throat in this patient (premise a) implies strep throat (premise b). The patient has enlarged lymph nodes, fever, and sore, inflamed exudate-​ covered throat (premise a). Therefore, the patient has strep throat (premise b). This argument is of the form if a implies b is true and a is true, then b is true, which is valid by modus ponens. More often the argument (Argument 5.9) is constructed as: Argument 5.9 If the patient has strep throat (premise a), then there should be enlarged lymph nodes, fever, and sore, inflamed exudate-​covered throat in this patient (premise b). The patient has enlarged lymph nodes, fever, and sore, inflamed exudate-​ covered throat (premise b). Therefore, the patient has strep throat (premise a).

Deduction, Induction, and Abduction

The aforementioned argument is of the form if a implies b is true and b is true, then a is true, which is invalid and is the Fallacy of Confirming the Consequence. The premise b could be true for any number of reasons besides premise a. The Fallacy of Confirming the Consequence is arguably to blame for most misdiagnoses. Nearly 25% of patients with Parkinson’s disease, for example, are misdiagnosed and instead have tremor due to essential tremor, hyperthyroidism, the influence of drugs, or some other cause. Physicians usually reason that if a patient has Parkinson’s disease (a), then she should present with tremor (b). The presence of Parkinson’s diseases implies the presence of tremor (a implies b). A patient who presents with tremor establishes b as true, and a is taken as true on the basis of the truth of b. At least 25% of the time, however, employment of such reasoning leads physicians to incorrect diagnoses, which in turn lead to incorrect treatments and patients’ continued suffering.

Rescuing the Hypothetico-​Deductive Method The Fallacy of Confirming the Consequence may be avoided by altering the original form—​if a implies b is true and b is true, then a is true—​to the following form: if and only if a is true, then b is true. Corresponding to this form is the following argument: if and only if the patient has strep throat (a) is the (implies) throat culture is positive (b), then the patient has strep throat (a), and the Fallacy of Confirming the Consequence is avoided. However, in practice, the specificity of the throat culture is never 100% and thus there may be false positives. A patient may have a positive throat culture without infection, suggesting that bacteria are present but are not causing an infection. As discussed previously, the argument can be given more utility by conversion to the probability syllogism supported by statistics. Nonetheless, the basic epistemic structure of the argument is maintained. The converse is true as well. The negation of a, that is, a is false, in the argument if and only if a implies b is true and b is false (negation of b) necessarily means that a is false. The claim is made that if the throat culture is negative, then the patient cannot have strep throat. The failure of the Streptococcus to grow in the culture medium, however, may be due to one of any number of other reasons. The patient may have had already begun a course of antibiotics, for instance, or the Streptococcus, although present, had not yet caused an infection but nonetheless produced the result of a positive culture. Again, the extrapolation to probability syllogism bolstered by statistics can increase the utility of the argument. The importance of the if and only if logic highlights the importance of the differential diagnosis, which is a list of all reasonably likely candidates. With regard to tremor, other reasonably likely candidates to the preponderant diagnosis of Parkinson’s disease include, but are not limited to, essential tremor,

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drug-​induced tremor, drug-​withdrawal tremor, physiological tremor, and hyperthyroidism. Taken together, Parkinson’s disease and the other candidates constitute the differential diagnosis. The validity of a diagnosis of Parkinson’s disease in a patient with tremor depends on the extent to which other reasonably likely candidates have been excluded as possibilities. For this reason, a differential diagnosis is perhaps more important than the eventual diagnosis. However, my experience has shown that only rarely do clinicians consider a differential diagnosis seriously. In light of this fact, the high rate of misdiagnosis comes as no surprise (in the United States, failure to diagnosis is the leading cause of medical malpractice complaints). The importance of the if and only if logic can occasion a logical fallacy called the Fallacy of Limited Alternatives, where it corresponds to the following form: If a or b or c implies d is true, and d is found true while b and c are found false, then a must be true. However, the argument is invalid. With essential tremor (b) and drug-​induced tremor (c) ruled out, the implication that the tremor of the presenting patient is due to Parkinson’s disease (a) is deemed true because it is the sole remaining alternative. Yet nothing about b or c bears on the truth or falsity of a. With essential tremor (b) and drug-​induced tremor (c) ruled out, it appears reasonable to conclude that the presenting patient’s tremor (a) is due to Parkinson’s disease (b). However, a number of other reasonably likely alternatives—​physiological tremor, hyperthyroidism, and drug withdrawal, for example—​never enter consideration. As can be appreciated readily, the Fallacy of Limited Alternatives becomes the Fallacy of Confirming the Consequence. This problem is well demonstrated in patients with cervical dystonia. Such patients present with unusual twisting of the neck, a symptom that many physicians have little experience with. Although cervical dystonia is a real dis­ ease, a large percentage of patients are diagnosed as psychogenic. Sadly, attribution of psychogenicity wastes time, squanders effort, and damages patients’ self-​ esteem. A  delayed but eventually correct diagnosis still undermines patients’ confidence in the medical profession. Because nearly every physician is aware of psychogenic disorders, psychogenicity is an element in the differential diagnosis of every disorder. For physicians unfamiliar with cervical dystonia, the diagnosis of psychogenicity remains the only element in the differential diagnosis, as the physician does not know what other conditions to consider (discussed further in Chapter 9). The Fallacy of Limited Alternatives thus leads the physician to believe the incorrect diagnosis. One can “fuzzify” the Principle of the Excluded Middle in the argument if (a or b or c) implies d is true, and d is found true while b and c are found false, then a must be true by constructing the following argument: a implies a 90% probability of d, b implies a 50% probability of d, and c implies a 5% probability of d, therefore a is the more likely cause of d. However, it is critical to note that the 90% probability of a being true has no effect on the 50% probability of b being true or the 5% probability of c being true, assuming that the probabilities

Deduction, Induction, and Abduction

of b, c, and d are independent of each other. If the physician were to only test for the condition of a being true, believing a to be the most likely condition associated with a, then the physician, de facto, is excluding the possibility of c or d being true as well. However, a being true does not change the probabilities of c or d being true even if a was found false. If the physician were to only consider c implying d in the event of a being false, this would be illogical because the consideration of b implying d is independent of any consequence of c or a being found true or false. Extrapolation of the Fallacy of Limited Alternatives to probability leads to the Gambler’s Fallacy discussed later. The critical point of the discussion just given is that, even though assigning probabilities violated the Principle of the Excluded Middle, the logical structure remains intact.

Abduction The Fallacy of Confirming the Consequence represents a form of reasoning known as abduction. Fundamental to medical diagnosis, abductive reasoning has a long history. Indeed, the remarkable advances in anatomical and histological pathology occurring in the mid-​nineteenth century installed allopathic medicine over homeopathic and other kinds of medicine as the preferred form and served further to enshrine abduction as a method of medical reasoning (see Chapter 6). The ascendancy of allopathic medicine meant a massive increase in the Fallacy of Confirming the Consequence’s impact on diagnosis, a development that came arguably to the detriment of physicians, healthcare professionals, and patients. This impact endures in the very method of teaching at many medical schools.

Principle of Transitivity and the Fallacy of Pseudotransitivity An important and powerful propositional logical principle, the Principle of Transitivity corresponds to the following form:  if a implies b is true and if b implies c is true, then a implies c is true. The Principle of Transitivity helps discover knowledge claims that had otherwise gone unrecognized. For example, it might have been well known that a implies b and that b implies c, but the fact that a implies c was not recognized. Huntington’s disease, a hyperkinetic disorder that manifests as involuntary uncontrollable movement, presents a useful example. Tetrabenazine, a drug that reduces involuntary movement associated with Huntington’s disease, lends itself to the construction of the following claim: tetrabenazine implies control of involuntary movements is true, and invol­ untary movement implies (Huntington’s disease or tardive dyskinesia or dystonia or . . .) is true, then tetrabenazine implies the treatment of (Huntington’s disease or tardive dyskinesia or dystonia or  .  .  .)  is true. Consequently, tetrabenazine

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should be considered for all these causes of involuntary movement and might be an example of an off-​label use (for tardive dyskinesia or dystonia or . . .) of an approved drug (for Huntington’s disease). To deny the conclusion tetrabenazine implies the treatment of (Huntington’s disease or tardive dyskinesia or dystonia or . . .) is true is to deny the premise involuntary movement implies (Huntington’s disease or tardive dyskinesia or dystonia or. . . .) That is, involuntary movement associated with Huntington’s disease is perceived as being different, in terms of the effects of tetrabenazine on it, from involuntary movement associated with other conditions. This determination is an empirical matter; in other words, its truth or falsehood depends on testing and observation. However, it just may be that the involuntary movement of Huntington’s disease is not the same as tardive dyskinesia or dystonia or. . . . In this case, the argument tetrabenazine should be considered in the treatment of tar­ dive dyskinesia or dystonia or . . . based on its effectiveness in patients with Huntington’s disease would be an example of the Fallacy of Four Terms. In this form, the argument is the Fallacy of Pseudotransitivity and is of the form if a implies b is true and c implies b is true, then a implies c. In this case, tetrabenazine implies improvements of involuntary movements (of Huntington’s disease), and tardive dyskinesia or dystonia or . . . implies invol­ untary movements; then tetrabenazine implies tardive dyskinesia or dystonia or. . . . This conclusion is not valid, and the truth or falsehood of the claim cannot be determined. The Fallacy of Pseudotransitivity is fundamental to metaphor; that is, it is very much involved in medical decision-​making and medical science (Chapter 12). The Fallacy of Pseudotransitivity is fundamental to the off-​label use of drugs and devices approved by the US Food and Drug Administration (FDA). The FDA may approve a drug or device for a particular disease, but clinicians have the authority to use the same drug or device for other conditions on which the FDA has established no position. In a compellingly real way, the application of the Fallacy of Pseudotransitivity serves to benefit many patients, thus demonstrating the utility of the judicious use of a logical fallacy. In 1934, Dr. Mary Walker observed similarities between symptoms of surgical patients whom she had treated with curare-​like compounds and symptoms of patients with myasthenia gravis. The effects of the curare-​like compounds on the former were reversed with the administration of anticholinesterases. This remedy lends itself to the following analogy: because anticholinesterases are to weakness induced by curare-​like drugs and as the weakness induced by curare-​like drugs is to myasthenia gravis, anticholinesterases are to myasthenia gravis. This analogy rests on the Fallacy of Pseudotransitivity. No prior reason exists to assume that there is any causal similarity between weakness induced by curare-​like drugs and weakness caused by myasthenia gravis. For example, stroke causes weakness as well. Commission of the fallacy nonetheless led to a hypothesis that, once tested and validated by subsequent research, ushered in

Deduction, Induction, and Abduction

treatment with anticholinesterases and increased the survival and wellness of many patients with myasthenia gravis. Many scientists, physicians, and healthcare professionals invoke the logical form of the Fallacy of Pseudotransitivity to support an argument. This they do wrongly because the form offers no such support. In fact, its invocation may set science in medicine on an incorrect and potentially disastrous course, at least for some patients.

Induction In contrast to deduction, induction is the process of generating a principle from a set of particulars. If, for example, a scientist, in defining ravens as a large bird with a particular form of a beak and a distinctive call, observes that every so defined raven is observed to be black in color, a new definition of raven is possible, which is a raven also black in color. The definition of ravens now contains new knowledge: that is, their black color. Evidence that this generates new knowledge is found in the answer to a question one might pose whether ravens that someone has not actually seen are black. Thanks to induction, the answer in most instances will be “yes.” Although induction does create the possibility of new knowledge, it does not create certain knowledge. That is, it cannot be known whether, somewhere beyond an individual’s experience, an observation exists that falsifies an induction—​a raven of some other color than black, for example. This ultimate uncertainty informs the Fallacy of Induction. Unfortunately, many physicians, healthcare professionals, and scientists fail to appreciate this fact. A single contrary instance disproves an induction because, as a logical proposition subject to the Principle of the Excluded Middle, the induction must be true (admitting no single false case) or false (admitting no single true case). Although the Fallacy of Induction prevents unequivocal certainty, it does permit some probability of certainty. Consider the toss of a coin: the induction from experience is that 50% of the flips will result in a “heads” and 50% will result in a “tails.” How many times must the coin be flipped to be sure of the probabilities of “heads” or “tails”—​what if both sides of the coin are “heads?” A fair coin must be tossed five times in order to establish that it has at least a 95% chance of actually being heads on both sides. To be 95% confident that the probabilities of heads and tails are exactly 50% each would require 10,000 tosses. The Fallacy of Induction is mitigated by performing the requisite number of coin tosses but only after the prior establishment of a threshold or cutoff. Enumeration by counting is one method of generating a general principle or hypothesis: for example, all ravens counted are black. Another method, the direct method of agreement (Mill, 1843), may be articulated in the following way: every time an agent is present, a specific consequence is realized. Every

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time a specific consequence is realized, the agent is or was present. The direct method of agreement is analogous to the notion of necessary and sufficient conditions. It may be argued, for example, that every time patients present with enlarged lymph nodes, fever, and sore, inflamed, exudate-​covered throats, their throat cultures also test positive for Streptococcus. Also, every time patients’ throat cultures tested positive for Streptococcus, they presented with enlarged lymph nodes, fever, and sore, inflamed, exudate-​covered throats. For these observations of specifics, the generalization may be induced that all patients have strep throat who present with enlarged lymph nodes, fever, and a sore, inflamed, exudate-​covered throat. Operations on inductions also can establish relations among the premises induced. Chief among those relations is the notion of causation. These methods include (1) method of agreement, (2) method of difference, (3) joint method of agreement and difference, (4) method of residuals, and (5) method of concomitant variation. Each of these will be explained where applicable in the text. Mill’s method of difference may be articulated in the following way: every time an agent was present, a specific consequence occurred, and every time a spe­ cific consequence was not observed, the agent was absent. From a set of these observations, it may be generalized that the agent is associated with (and perhaps caused) the specific consequence. A third method developed by Mill, the joint method of agreement and difference, may be articulated in the following way: if agents a, b, and c are associated with consequences x, y, and z, and if, in other situations, agent a, d, and e are associated with consequences x, w, and t, then agent a is associated (perhaps the cause or consequence) with x. A fourth method developed by Mill, the method of residue, rests on the following argument: agents a, b, and c are associated with consequences x, y, and z. If agent b is the necessary and sufficient condition of consequence y, and agent c is the nec­ essary and sufficient condition of z, then agent a can be induced to be a sufficient condition of x. Most of the methods of induction given here relate primarily to dichotomous conditions; namely, the presence or absence of agents and consequences. Such conditions may not always be in place, particularly in situations that involve continuous variables. A  fifth method developed by Mill, which can be applied to continuous variables, the method of concomitant variations, rests on the following argument: the consequence that varies in a manner similar to the manner in which an agent varies is related to that agent. Note that the variations could be when the agent goes “up,” the consequence goes “down” or the opposite. This is analogous to correlations as a method of establishing a principle from a set of observations in which the value of the individual observations varies over a course of observations. A major limitation of induction is that it speaks solely to associations or correlations; it justifies no inference as to causality between the objects

Deduction, Induction, and Abduction

involved. It may be the case that there exists, unknown to the observer, an agent that causes the associations between the observables. For example, some agent q causes agent a, as well as consequence x, while agent a actually is unconnected to consequence x. Nevertheless, agent a will appear to be connected with consequence x. As the eighteenth-​century British philosopher David Hume (1771–​1776) observed, the issue is whether agent a is a constant conjunction with consequence x or is a necessary connection with consequence x (Hume, 1739).

Induction and the Scientific Method Induction is commonly equated with empiric science as promulgated by the seventeenth-​century English Sir Francis Bacon (1561–​1626) in his 1620 work Novum Organum: True Directions Concerning the Interpretation of Nature. The attraction of Bacon’s method lies in its appearance of being solely driven by data or observations, and thus championed observations and the scientific method (experiments) while rejecting reasoning from principles. This notion has endured to the present day. It informs so-​called evidence-​based medicine, holding randomized controlled trials as the only legitimate source of medical knowledge, according to some. Evidence-​based medicine receives further discussion later (Montgomery and Turkstra, 2003). The critical question is whether induction is, in fact, uncontaminated by theory. Does induction follow strictly from the facts, or does some a priori principle or perspective shape it? Some insist that “the facts speak for themselves.” Induction begins with identifying a set of observations from which some common feature or property is then taken as a general principle. All ravens ever observed (set of observations) have been black in color (common feature), and all ravens are therefore black (general principle). Yet what accounts for how observations are gathered in a set from which the induction is derived? Why are certain observations entertained but not others? The induction that all ravens are black has more to do with how a group of entities were assembled as ravens than with any absolute property of ravens. Thus, the definition as to what constitutes a raven does not arrive spontaneously from any set of observations. It is drawn, rather, from a general principle derived from some other prior induction, which in itself may be derived from some prior induction. The necessity of some prior general principle establishing a set of particular observations as related and relevant prior to any induction is the A Priori Problem of Induction. It seems rather a simple and straightforward matter to point out that the property of blackness cannot be part of a definition of a raven that motivates the induction that all ravens are black. But what if it was neither so clear nor so simple? What if that which was responsible for the blackness of ravens somehow

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affected the definition in such a way as to reveal the induction as actually a tautology, a self-​fulfilling prophecy, or an instance of question-​begging? In this case, the facts cannot speak for themselves for they resulted from prejudicial presuppositions. Subsequent research based on faulty induction would be at risk for unproductive irreproducibility.

6

Evolution of Medical Reasoning

In this class, we are going to study the diagnosis of Parkinson’s disease. This disease is due to degeneration of the dopaminergic neurons in the substantia nigra pars compacta of the basal ganglia in the brain. If the patient has Parkinson’s disease, the patient will have tremor, slowness of movement, and increased resistance to a passive range of motion, otherwise termed rigidity. STUDENT: Doesn’t essential tremor cause tremor, cause slowness of movement, and change the resistance to passive movement? PROFESSOR: That is for another lecture. STUDENT: Wouldn’t it make more sense to start with a discussion of tremor and then proceed to the various disorders that produce tremor? PROFESSOR: That has not been our tradition. PROFESSOR:

There always has been and likely will continue to be a number of different programs or schools of medicine that compete, sometimes fiercely. Indeed, one might think that these schools derive from completely different universes. As they did not, there is nothing fundamentally different about a patient who sees an allopathic physician from one who sees a homeopath. It is not like allopathic medicine and homeopathy decided to divide the different diseases humans are heir to and so the patients similarly segregated themselves. Allopathic medicine has become so dominant since the Flexnerian revolution in medical education in the early 1900s that it is hard for many to appreciate that there are differing approaches. However, this may only appear so to allopathic doctors of medicine (MDs) and osteopathic medicine (DOs) as there is a vast underground of patients acting as their own herbalists, and chiropractic continues unabated, if only through tradition and antidiscrimination laws. Thus, there continue to be strains of alternative approaches to traditional allopathic medicine, and many of these previously alternative approaches are finding their way into allopathic medical practice.

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The question becomes:  What is the epistemic basis for the different approaches, and why has the division been impossible to maintain sharply? The argument here is that all clinicians faced the same epistemic question, which is how to make sense of the individual patient given the great diversity among patients. Furthermore, medicine is forced to dichotomize: Is there or is there not a disease; does one treat or not treat? The question for every clinician is how to make sense of it all despite the bewildering variations in human health and disease. As will be addressed in more detail, the fundamental question posed by the heterogeneity of humans in health and disease is whether the heterogeneity represents variability or diversity. The term “variability” (or “variance”) implies some variation around an archetypical or canonical form(s), such as some measure of central tendency—​a fundamental presupposition in rationalist/​allopathic medicine. By contrast, the term “diversity” can be taken to imply that each individual is a case unto itself—​humanness is highly idiosyncratic, as is held in empiric medicine. Rationalist/​allopathic medicine must account not only for the archetypical or canonical form but also for the variance or deviation from the canonical form. The empiric would argue that there is more than a single canonical form underlying the variety of manifestations; indeed, so many canonical forms would be necessary for the explication of the individual patient that the notion of canonical forms is without sense. For the rationalist/​allopathic physician, even if there are multiple forms, the number of forms or factors must be economical; that is, a set of explanatory elements smaller than the number of individuals whose varying manifestations are to be understood.

Ascendency of Allopathic (Scientific) Medicine The Flexner Report (Flexner, 1910) dramatically reshaped medicine in North America. Ostensibly, an evaluation of medical education with recommendations ultimately led to the closure of nearly half of the medical schools in the United States. Those remaining did so by allying with universities—​mostly for financial necessity rather than by force of medical knowledge. In so aligning, the majority of medical schools fell into the allopathic traditions. With education being a critical requirement of medical licensure, the allopathic physicians acquired great power. Many traditional medical schools teach diseases proceeding first from the principles, particularly pathology. Indeed, it may well be that histopathology, derived from cell theory in the mid-​1800s and the clinical–​pathological correlations of Sir William Osler (1849–​1919), did the most for allopathic medicine’s triumph over its competitors and shaped allopathic medical education to this day. Most courses are organized according to organ systems, with

Evolution of Medical Reasoning

the typical trajectory beginning with the normal structure of organs, to their alteration in disease, and then finally to the symptoms and signs associated with the disease. To be sure, the pedagogy is changing from a traditional 2 years of basic science followed by clinical apprenticeships to a problem-​or case-​based education. Nevertheless, the ontological presuppositions and epistemology approaches largely remain the same. While this approach of reasoning from anatomy to pathology and from physiology to pathophysiology appears logical, this is not the manner by which patients usually first present themselves to the clinician. Patients seldom come saying that their problem is one of degeneration of dopaminergic neurons in the substantia nigra pars compacta of the basal ganglia in the brain, the pathoetiology of Parkinson’s disease. Rather, patients present with symptoms and signs, such as tremor, slowness of movement, and increased resistance to passive movement.

The Inverse Problem Clinicians must, at least initially, reason from symptoms and signs to the pathological conditions in order to diagnosis and treat. Typically, there is not a one-​to-​one correspondence between a set of specific symptoms and signs and a specific diagnosis. It is rare for a set of specific symptoms and signs to be pathognomonic of only a single pathological process; hence the need for further considerations, particularly the generation of a differential diagnosis. Epistemically, the lack of a one-​to-​one correspondence between sets of symptoms and signs and the diagnosis relates to the inverse problem that arises when there is a single mechanism that mediates the expression of multiple causal factors. These factors may be parallel yet converge onto the common final pathway mediating expression, or the factors can be successive or sequential. An automobile may not start for any number of reasons, and the automobile just sitting there does not specify the exact cause. Clinicians recognize, if only tacitly, the inverse problem in the admonition of practical reasoning to entertain a differential diagnosis. Most clinicians approach the possible treatments in a more or less sequential fashion, recognizing that if multiple changes are made simultaneously, it will be difficult to attribute the exact cause of improvement or an adverse effect. Although not often appreciated, the inverse problem is ubiquitous. It is inherent in the Duhem–​Quine thesis, which holds that it is impossible to identify within the experiment or experience which of the several causes, conditions, premises, or propositions are at fault. The inverse problem contributes to the Fallacy of Limited Alternatives and its associated Fallacy of Confirming the Consequence in diagnosis. For example, there may be multiple causes of a sore

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throat, and just the appearance of a sore throat will not specifically identify the relevant cause. Hence, a differential diagnosis should be entertained. Sorting through the differential diagnosis risks error because of the Fallacy of Confirming the Consequence and the Fallacy of Limited alternatives and its probability counterpart, the Gambler’s Fallacy. The Gambler’s Fallacy may occur when the demonstration of one cause seems to affect the likelihood of other causes. Frequently, when confronted with a patient with a peripheral neuropathy and a known diagnosis of diabetes mellitus, the physician quickly attributes the peripheral neuropathy to the diabetes and does not look for other causes. However, there may be nothing about the patient having diabetes mellitus that reduces the probability of an alternative diagnosis, assuming the probability of each alternative is independent of the other. If independent alternative diagnoses, such as autoimmune disorders, are appropriate in the absence of diabetes mellitus, they are just as appropriate for consideration in the presence of diabetes. The pursuit of alternative diagnoses depends on factors independent of the presence or absence of diabetes mellitus. Generally, the deciding factors are ethical: How low does the prior probability of disease X have to be before the physician does not test for disease X? This situation may present a “slippery slope.” At what point does a physician stop testing for every disease that could cause a peripheral neuropathy short of bankruptcy? But this is an ethical question, not a scientific question (Chapter 3).

Historical Approaches to Diagnosis Since antiquity, there have been a number of approaches to diagnosis—​most prominent are the practices of the empirics and the rationalist/​ allopathic physicians. The empirics were skeptical that any scientific approach based on reasoning from an economical set of scientifically derived principles would ever be adequate to reconstruct the symptoms and signs of an individual patient. For the empirics engaged in homeopathy, the approach was to match the symptoms and signs to the effects of various treatments. Their motto was similia similibus curantur (“like cures like”). Those treatments with effects that most matched the individual patient’s symptoms and signs provided the diagnosis. But the diagnosis was highly idiosyncratic to the individual patient, which allowed no induction to general principles for which some economy of explanation could be achieved. Interestingly, the inverse problem was avoided. There were as many diagnoses as there were patients. Rationalist/​allopathic physicians believed that disease could be understood based on an economical set of fundamental mechanisms that would be combined to understand the patient’s symptoms and signs. Treatments were then based on the identified fundamental mechanisms. By an “economical set of fundamental mechanisms,” I mean that the number of principles necessary

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to explain health and disease in humans is sufficiently fewer than the number of humans. Nevertheless, infinity by recursion from the finite allows an explanation of the variety of patient manifestations just as an effectively infinite number of English language texts are possible using 26 letters, spaces, and punctuation marks. However, the rationalist/​allopathic physician took on the inverse problem. The consequence of that fateful choice to the epistemic question has enormous repercussions, which will be explored throughout this text. One could argue that the inverse problem may explain why allopathic physicians were and continue to be skeptical of putting too much stock in the patient’s specific symptoms and signs. It is easily appreciated why rationalist/​ allopathic physicians would dispense with a detailed understanding of the individual symptoms and signs, at least to some degree, and focus instead on the underlying pathology. Rather, symptoms and signs were organized based on their reference to underlying pathology, and, ultimately, it was the inferred pathology that guided diagnosis and treatment. Indeed, one of the greatest contributions of Sir William Osler, considered a paragon of the modern physician, was his ability to relate symptoms and signs to specific pathologies. Osler was able to do so because he was first a pathologist who conducted postmortem examinations on many of his patients. Empirics, one might say, embraced the diversity. In either case, the two approaches are opposite sides of the same epistemic coin (conundrum). Certainly, this can be off-​putting for the patient and give the appearance of arrogance on the part of the clinician (inviting polemical interpretations), but it is not at all clear, as a matter of practice or principle, that exhaustive defining and analysis of symptoms and signs is critical—​or so the argument may be made. I, however, find that symptoms and signs are information just like any laboratory test and often less expensive to obtain. The more information, the less likely one is to befall the Fallacy of Limited Alternatives and to establish prior probabilities that mediate the specificities and sensitivities into positive and negative predictive values critical to medical decision-​making. Certainly, a disregard of any of the patient’s symptoms and signs could affect the rapport between patient and clinician, which is critical to the effective practice of medicine.

Medicine and Science Medicine has had a long and complicated relationship with science (see Chapters 3 and 5). There is the temptation to consider modern medicine as synonymous with modern medical science. However, that would be only a recent invention and only questionably true. Allopathic medicine has been known since the Greeks, at least as represented by the works of Galen (126–​216), which were derived from the physics and metaphysics of Aristotle. Aristotle

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was a keen empiricist but also a rationalist, materialist, and reductionist. While the actual science claimed by medicine has changed dramatically, the essential relation of medicine to science continues, no matter the definition of science. This raises the following question: If science at time A, which sees disease as a consequence of humor imbalances, as per Galen, is different from science at time B, which sees disease as a consequence of abnormal extension or contraction blood vessels, as per Benjamin Rush (1746–​1813), then what is it that accounts for the continued affection by the allopathic physicians for science when, at each time, the sciences seemed so different? The mid-​1800s was a remarkable time in the history of medicine. Early in the century, a number of alternative medical paradigms challenged the rationalist school (Coulter, 1982). The second half of the century found these alternatives dramatically weakened and relegated to a fringe, for better or worse. The new mainstay medicine, also called regular, allopathic, or rationalist, would become essentially unchallenged. As will be seen, the reason for the ascendency of regular or allopathic medicine had little, if at all, to do with its greater benefit to the patient, but rather was political and affected under the guise of educational reform. To be sure, allopathic medicine would be vindicated by modern medicine, but this was not the case when allopathic medicine became dominant. The questions become: How did allopathic medicine differ from its competitors, and how does this relate to the approach to knowledge?

Diagnosis and Treatment In the history of medicine there has been a continual divide between those who diagnose and those who treat. The Oath of Hippocrates states “I will not cut for stone, even for patients in whom the disease is manifest; I  will leave this operation to be performed by practitioners, specialists in this art.” The stone usually refers to bladder stones, and their removal was referred to surgeons. In early England, the distinctions of practice were sharp, with healthcare divided among physicians, who diagnosed; surgeons, who operated; and apothecaries, who dispensed medications based on the diagnosis provided by the physician. In England, the system evolved to where surgeons and apothecarists merged in what would become general practitioners, typically outside of hospitals, and physicians who remained primarily hospital-​based. In America, the distinction broke down, with physicians also having to fulfill the role of surgeon, at least with respect to the practice of bloodletting. The privilege of diagnosis was ardently defended as reserved for physicians. Apothecarists were prohibited from making diagnoses, although the apothecarist would compound the various treatments prescribed by the physician. Indeed, apothecarists could not bill patients for any diagnosis they

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rendered in the course of providing various elixirs, poultices, and plasters. The Rose case (1701–​1704) in England allowed apothecarists to diagnosis in the course of their preparing treatments, although they could not bill for the service; diagnoses for fees were reserved for physicians. The revised code of ethics of the American Medical Association (AMA) in 1903 specifically called for the prohibition against pharmacists making diagnoses and the obligation of pharmacists to dispense precisely what the physician prescribes. The situation continues to the present day, where the ability of nonphysicians to make diagnoses is strongly circumscribed by law and practice. For example, only 18 states, as of 2013, allow physical therapists unrestricted access to patients, meaning that referral by a physician typically is necessary (https://​ www.apta.org/​uploadedFiles/​APTAorg/​Advocacy/​State/​Issues/​Direct_​Access/​ DirectAccessbyState.pdf).

Evolution of Medical Science Galen’s inferences from observations were to qualities of moist versus dry and hot versus cold. As can be seen in Figure 6.1, these qualities were related

MOIST

air

Water

choleric

phylegmatic melancholic

COLE

y rit LY HOe)

(yel C low

fire

AU TU ma M tu

(bluANC eb il

MEL

ER MM SU uth yo R LE d bile HOor re

Sangunia

N

BL O

M EG

HOT

PH o L

OD

ld

SP RI ch ild

od ho

ER NT e ag

NG

W I

earth

DRY FIGURE 6.1 

Schematic representation of Galenic medicine that relates the four elements of observation (moist vs. dry, hot vs. cold), the four elements of Aristotle (earth, air, fire, and water), the four seasons, and the four humors whose imbalance resulted in disease. From Arikha (2008).

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to the four Aristotelian elements:  earth, fire, water, and air. Furthermore, these elements were related to the four seasons and to the four humors. The associations of these items were very important. For example, the conjunction of air, moisture, and the spring season figured very prominently in the diagnosis of yellow fever by Benjamin Rush (1746–​1813). Yellow fever is due to a viral infection transmitted by Aedes aegypti mosquitoes, which are most prevalent in wet seasons, such as the spring. Another, although often overlooked, concept Galen borrowed from Aristotle is the latter’s notion of contraries—​moist versus dry and hot versus cold. Aristotle held that all entities were some mixture of two extremes. “The physicists . . . have two modes of explanation,” Aristotle wrote. “The first set make the underlying body one—​either one of the three or something else which is denser than fire and rarer than air—​then generate everything else from this, and obtain in multiplicity by condensation and rarefaction. Now these are contraries which may be generalized into ‘excess and defect’ ” (Aristotle, 2001). The second set, according to Aristotle, consists of “contrarieties [that] are contained in the one and emerge from it by segregation” and that also produce “other things from their mixture by segregation” (Aristotle, 2001). Aristotle dismissed the second set. Dichotomizing forces and entities achieve a great economization of underlying mechanisms and principles; hence its attraction to scientists, philosophers, and allopathic physicians. Consider a continuum such as shades of gray: great economy is achieved by positing each shade of gray as some mixture of black and white rather than each shade constituting a separate and individual entity. The problem with the latter position is that each instance of gray is unique; consequently, there is a potentially infinite number of shades of gray. The appearance manifests some combination of the dichotomous entities. Hegel (1770–​1831), who argued that the “truth” consisted of some synthesis of theist and antithesis, also advanced this notion. Galen extended the notion in that he believed all humans are affected by some combination of four humors, to parallel Aristotle’s four elements as described previously. Disease was then either too much or too little of specific humors, and treatment was to restore the balance by either removing excess, such as through bleeding, cupping, cathartics, and diuretics, or increasing them to relieve the paucity. Galen also went on to attribute diseases as some imbalance of humors within a specific organ system, sympathetic to the rationalist/​ allopathic intellectual tradition. While the notion of contraries or one-​dimensional push–​pull systems may seem quaint or only historical curiosities, they are still fundamental to medical knowledge and biomedical science. Genetic disorders represent gain of function or loss of function (Ségalat, 2007). The gain of function represents an

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abnormally greater effect of the gene with toxic effects or an abnormally lesser effect of a gene and consequent failure of the system. Such simple dynamics bred optimism for the rapid development of genetic therapies and perhaps helped to justify the Human Genome Project (1990–​2003), estimated to cost more than $3 billion dollars. There is little question that the Human Genome Project has increased the diagnosis and understanding of genetic abnormalities greatly; however, the contribution to new treatments is far less spectacular (Le Fanu, 2012). To date, there have been only two US Food and Drug Administration (FDA)-​approved gene therapies. Of the 47 drugs approved by the FDA since 1996 for genetic disorders (https://​www.centerwatch.com/​drug-​information/​ fda-​approved-​drugs/​therapeutic-​area/​34/​genetic-​disease), only 10 were directly related to a genetic disorder that did not have some prior precedent. For example, the list of 47 includes agents that preexisted but now were being manufactured by recombinant technology. Others were symptomatic treatments for many applications, and approval for a genetic cause could be considered an add on. Perhaps the problem lies in thinking that the physiology and pathophysiology of genetic disorders are as simple as one-​dimensional push–​pull systems, where gain of functions can be blocked and a loss of function can be supplemented easily. Perhaps one of the most optimistic expectations that benefited from the very large investment of the Human Genome Project was the ability to identify genetic abnormalities that would guide cancer treatment. To date, the results have not been overwhelming (Prasad, 2016). Other important disorders with one-​dimensional dynamics include the dopamine theory of schizophrenia, the norepinephrine theory of major depression, the neuronal overactivity of the globus pallidus interna in Parkinson’s disease, and the underactivity of the globus pallidus interna in hyperkinetic disorders, such as Huntington’s disease. More recently, neuronal overactivity is being replaced by excess beta oscillations in some aspects of neuronal activities. One-​dimensional push–​pull dynamics is also seen in some cognitive or personality disorders associated with frontal lobe injuries, where the normal function of the frontal lobes to suppress what might be described as more primitive behaviors is lost. Most striking is the conceptual underpinnings of neurometabolic imaging, such as functional magnetic resonance imaging and positron emission tomography scans, where a relative excess or deficiency of blood flow (or other markers of metabolism) drives inferences about the pathophysiological mechanisms. Abraham Maslow (1908–​1970) wrote to the effect that “if all one has is a hammer, the whole world appears as a nail” (Maslow, 1966). Since Aristotle, one-​dimensional push–​pull dynamics has been a very big hammer, so it is not surprising that disease mechanisms likewise would be couched in terms of relative deficiencies or excesses.

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The Persistence of Galenic Ideas with the Advancement of Medical Science Following the discovery of circulation by William Harvey (1578–​ 1657), physicians developed a causal theory of disease based on abnormalities in the circulatory system. Not surprisingly, Benjamin Rush (1746–​ 1813), a leading physicians and instructor of physicians, held that disease represents convulsions of blood vessels. For its treatment, Rush did what most Galenists would do, bloodletting. Even Sir William Osler, a paragon of physicianhood, still prescribed bloodletting, despite the remarkable advances in the clinical–​ pathological basis of disease to which he made significant contributions (Bliss, 2000). Aristotle’s notion of the contraries still affects medicine, particularly neurology. John Hughlings Jackson (1835–​ 1911) argued that neurological symptoms were the result of excessive or deficient function of specific neurological structures. This conception is strikingly parallel to the gain of function and loss of function used to explain genetic disorders. Clearly, Hughlings Jackson’s notions predated that of genetics; however, all this means is that both the genetic notions and those of Hughlings Jackson have a common parent: the epistemic conundrum that led Aristotle to his contraries. Paralysis was viewed as a deficiency of function in those structures that generated movement, whereas seizures were considered excessive function in those same structures. M. R. Walshe, a leading British neurologist in the mid-​ 1900s, argued that beriberi could not be due to a deficiency of vitamin B1 (thiamine), which is the case. Walshe wrote: It is not too far-​fetched, perhaps, to recall in this connection a famous axiom [note not hypothesis or theory—​author] of Hughlings Jackson with regard to cerebral disease. He said that negative or destructive lesions could not cause positive symptoms, but might allow of their development—​in other words two factors were concerned in the production of spastic paralysis, destruction of pyramidal fibers causing the negative symptom paralysis, and consequently unbalances activity of other centers causing positive the symptom spasticity. . . . So with beriberi, absence of vitamine cannot be an adequate cause of polyneuritis, which is clearly a positive reaction to a direct and positive cause. (Quoted in Phillips, 1974) Walshe was wrong. The development of deep brain stimulation (DBS) surgery, which treats neurological and psychiatric disorders by providing chronic and continuous electrical stimulation of specific regions of the brain, was hampered by the prevailing notion that DBS inhibits the stimulated structure as its mechanisms of action (Montgomery, 2012). This false notion derived from a demonstration of the benefits of prior surgical ablation of various brain structures for

Evolution of Medical Reasoning

neurological and psychiatric disorders and then equating the benefit of DBS to an equivalent mechanism; thus, the Fallacy of Four Terms (discussed in detail in Chapter 3). Interestingly, Helen Mayberg and colleagues demonstrated increased cerebral blood flow (inferred as increased neuronal activity) in area 25 G of the anterior cingulum in patients with treatment-​resistant depression. Those observations led to the hypothesis that DBS should inhibit area 25 G of the anterior cingulum and thus improve depression. Thus far, early open-​label clinical trials have been very encouraging; however, follow-​up blinded studies have been less successful. Mayberg and colleagues were perhaps right (if corroborated) but for the wrong reason, as a plethora of data indicate that the main action of DBS is excitation (Montgomery and Gale, 2008). The difference between this and the approach to Parkinson’s disease was that the logical fallacies in the reasoning of Mayberg and colleagues led to a hypothesis being tested, rather than the fallacious inference in the case of Parkinson’s disease taken as a fact (actually, as a quasi-​fact; a quasi-​fact is an inference that has the epistemic force of fact) and competing alternatives dismissed on the presumption of that quasi-​fact. Another example of contraries is the finding that animals given reserpine displayed symptoms that were thought similar to Parkinson’s disease, which responded to levodopa. Reserpine was found to block dopamine. Subsequently, dopamine was found deficient in patients with Parkinson’s disease. These observations led to the inference that Parkinson’s disease is a dopamine deficiency state. However, flooding the brain with dopamine through administration of its precursor levodopa or by transplantation of fetal dopamine neurons does not reverse the symptoms, particularly in more advanced cases. Despite these counterexamples, the notion of Parkinson’s disease as a dopamine deficiency state persists. Patients with all the symptoms and signs of Parkinson’s disease whose symptoms and signs improved with drugs that act like dopamine were not found to have dopamine deficiency on imaging studies. These patients are called scans without evidence of dopamine deple­ tion (SWEDD). Rather than surrender the notion of Parkinson’s disease as a dopamine-​deficient state, these patients’ symptoms were called something else, such as dystonic tremor (Bajaj et al., 2010; Lee et al., 2014). The discussion of SWEDD demonstrates the differences between rationalist/​allopathic physicians and empirics. For empirics, the symptoms and signs would dictate the diagnosis regardless of the underlying dopamine state. For allopathic physicians, the underlying pathoetiology determines the diagnosis. The implications are significant. Does this mean that every patient with the potential diagnosis of Parkinson’s disease must have a scan demonstrating a dopamine deficiency? Such a recommendation would be highly problematic given the prior probabilities of idiopathic Parkinson’s disease and the relative rarity of SWEDD (on the order of 15%). Depending on the specificity and sensitivity

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of the dopamine-​imaging test, it is likely that more patients with idiopathic Parkinson’s disease would be diagnosed with SWEDD than would patients who actually have SWEDD. However, it simply is not possible to estimate the specificity and sensitivity because the only defining criterion for the diagnosis of SWEDD is an abnormal dopamine-​imaging scan. Thus, the diagnosis would be tautological. The most important question is what difference does it make to differentiate patients with idiopathic Parkinson’s disease from those with SWEDD? If there is no difference in treatment, there seems to be little value in the differential diagnosis. Instead, one would just treat based on the symptoms and signs alone. This very reasonable medical approach is far more consistent with the empirics than with the rationalist/​allopathic physicians. Perhaps the only value may be in prognosis, where SWEDD appear to progress slowly. For clinical trials that aim to slow disease progression, enrolling patients with SWEDD would increase the variance and decrease statistical power, thus necessitating larger sample sizes and expense. The early development of treatments followed more the empirics than the rationalist school of thought. Even until the early 1900s, the treatments offered by the empirics and the rationalists were the same in many ways (Coulter, 1982). Most of these came directly from Galen. Sir William Osler, a paragon of modern medicine, recommended bloodletting and baths, as did the irregular or alternative medicine Thomasonians (Bliss, 1999). The primary means of treatment that bear any resemblance to current medicine in the 1800s were vaccinations, public health measures, and arsenicals. Vaccinations followed from the work of Edward Jenner (1749–​ 1823) who inoculated an 8-​year-​old boy against smallpox in 1796, although evidence exists for much earlier use vaccinations. The explanatory germ theory and immunology came much later. While there is a long history of ideas related to noxious agents as a cause of disease, it was not until the late 1800s that Robert Koch’s postulates provided a firm experimental basis for microbiology and dis­ ease. Using epidemiology and not bacteriology, in 1854, John Snow provided convincing evidence of an infectious agent spread by fecal contamination as the mechanism for cholera outbreaks. Compounds containing arsenic were used for disease, particularly syphilis, since the 1700s. It was not until 1909 that an arsenical, salvarsan, was demonstrated effective against the syphilis spirochete. Foxglove (digitalis) was used to treat failing hearts at least since 1785. Willow bark (aspirin) was used at least since the age of Hippocrates, and bark of the cinchona tree (quinine) was used to treat fever that developed in patients near swamps and marshes since the 1600s. Many modern treatments were clearly due to happenstance. The discovery of penicillin is a prototypical example. Indeed, much of the subsequent development of antibiotics attempted to replicate that happenstance and is best described as a “shotgun” approach. An example is collecting as many soil

Evolution of Medical Reasoning

samples as possible to find fungi that may have antimicrobial properties (Ling et al., 2015). Many of the current treatments of hyperthyroidism, diabetes, and hypertension followed from the use of sulfonamides by happenstance rather than rational medical science, as discussed in Chapter  15 (Le Fanu, 2012). Nonetheless, rationalist/​allopathic medicine appears to have taken credit.

Dominance of Mechanistic Theories of Physiology and Pathophysiology The discussion that follows demonstrates the powerful appeal of intuition (Johnson-​Laird, 2008). There are innumerable examples of how notions that just make sense intuitively dominate despite contrary evidence; for example, the issue of early detection of breast cancer in women between the ages of 40 and 49 (see Chapter 2): it just makes sense that the earlier the diagnosis, the better the prognosis, evidence to the contrary notwithstanding. A striking example is the resurgence of interest in surgical ablation of the globus pallidus interna (pallidotomy) for the treatment of idiopathic Parkinson’s disease in the 1980s. Pallidotomies were used, although rarely, since the 1950s. So, the question is, why the resurgence of interest? There was no significant change in the methodologies or sudden realization of the need. The limits of pharmacological therapies such as levodopa have been recognized since the 1970s. Rather, the globus pallidus interna rate theory (Chapter 12) provided an intuitive rationale, although wrong (Montgomery, 2012). The intuitive appeal of the globus pallidus interna rate theory follows from changes in the understanding of anatomy and physiology related to the cell theory and the derivative “neuronal doctrine” of the mid-​to late 1800s and the subsequent demonstration of neuronal inhibition by inhibitory neurotransmitters, as reviewed in Chapter 11.

History of Medical Abduction Perhaps one of the most central figures in the history of modern medicine is Galen, who lived from 129 to 216. It may seem strange to suggest that someone who lived more than 1,800 years ago was influential in modern medicine; however, his impact on medicine clearly continued in a direct way until the early 1900s and, in many implicit ways, continues to this day (Arikha, 2008). Galen was a scientist whose anatomical observations reinforced or contextualized Aristotle’s physics. Galen’s approach, and the approach of the vast majority of physicians to date, based diagnosis on abduction. Prior to Galen, Hellenic medicine could be broadly divided into the empirics, dogmatists, and rationalists. The primary defining difference was the route to certainty in medicine, whether that route was by observations of the idiosyncrasies of patients

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(hence empirics); based on principles that transcend individual differences, thus providing for an economy of diagnoses and treatments, as in the case of the rationalists; or in following formal algorithmic methods, as did the dogmatists (Zaner, 1998). Galenic medicine was taught in medical schools, and the graduate physicians were called regular practitioners of medicine or rationalists. Later, these physicians would be called allopaths, a term originated by Samuel Hahnemann (1755–​1843), considered the founder of homeopathy. There always have been alternative medical schools; however, they were not dominant until the 1840s, at least in the United States. Under the administration of President Andrew Jackson, a libertarian movement led to the repealing of medical licensure requirements in many states, thereby opening the field for alternative medicines. Homoeopathy was a major competitor of rationalist/​allopathic medicine in the early 1800s. In homoeopathy, an empiric discipline, the underlying mechanisms of disease are unknowable, or at least uncertain; thus, every patient becomes a unique case. In other words, the patient’s disease is A and not a mixture of B, C, and D where B, C, and D are thought to be general principles that apply to and thus explain any patient. Disease A is one of a kind, and thus there is only one patient with disease A. Allopathic physicians used abduction to diagnose and thus determine treatment. Aristotle, who provided the basis for Galen, wrote “the physician does not prescribe what is healthy for a single eye, but for all eyes, or for a determinate species of eyes” (“Posterior Analytics,” quoted by Zaner, 1998). Furthermore, None of the arts [techne] theorize about individual cases. Medicine, for instance, does not theorize about what will help to cure Socrates or Callias, but only about what will help to cure any or all of a given class of patients, this alone is its business. Individual cases are so infinitely varied that no systemic knowledge of them is possible. (“Rhetorics,” quoted in Zaner, 1998) Aristotle was very much in sympathy with the allopathic approach. Homeopaths rely on pattern recognition to match the patient’s symptoms to the effects of homeopathic treatments. Homeopaths did so because they believed that the symptoms and signs of disease represent the body’s attempts to rid itself of disease rather than the consequence of dysfunction. The presence of fever, seen as a defense mechanism, was to be encouraged. In fact, current science suggests that the empirics were correct (Kluger et al., 1996). For example, the homeopath would not likely provide aspirin to reduce fever as would an allopathic physician. The AMA was formed in 1847 following, and perhaps in reaction to, the formation of the American Institute of Homeopathy in 1844. This followed the infiltration of homeopathy into what otherwise would be allopathic practices. In 1847, the AMA Code of Ethics was adopted. There was clear antipathy against empirics and dogmatists, as seen here from the AMA Code of Ethics of 1847:

Evolution of Medical Reasoning

The first duty of a patient is, to select as his medical adviser one who has received a regular professional education [emphasis added]. In no trade or occupation, do mankind rely on the skill of an untaught artist; and in medicine, confessedly the most difficult and intricate of the sciences, the world ought not to suppose that knowledge is intuitive. Medical ethics cannot be so divided as that one part shall obtain the full and proper force of moral obligations on physicians universally, and, at the same time, the other be construed in such a way as to free society from all restrictions in its conduct to them; leaving it to the caprice of the hour to determine whether the truly learned shall be overlooked in favour of ignorant pretenders—​persons destitute alike of original talent and acquired fitness. The choice is not indifferent, in an ethical point of view, besides its important bearing on the fate of the sick themselves, between the directness and sincerity of purpose, the honest zeal, the learning and impartial observations, accumulated from age to age for thousands of years, of the regularly initiated members of the medical profession, and the crooked devices and low arts, for evidently selfish ends, the unsupported promises and reckless trials of interloping empirics, whose very announcements of the means by which they profess to perform their wonders are, for the most part, misleading and false, and, so far, fraudulent [emphasis added]. The US Supreme Court ruled that the AMA code of ethics was in violation of antitrust laws [Wilk v. American Medical Association, 895 F.2d 352 (7th Cir. 1990)], and the prohibition against fraternization with irregulars was removed from the code of ethics. Justification for the antipathy of allopathic physicians against the irregulars was on the basis that regular or allopathic medicine was based on science; from the AMA Code of Ethics of 1847: “A physician should practice a method of healing founded on a scientific basis; and he should not voluntarily professionally associate with anyone who violates this principle.” However, it must be admitted that the notion of science as it relates to medicine was very rudimentary at the time the mantel of science was claimed for allopathic medicine.

Institutionalization of Scientific Medicine and Further Reinforcement of Abduction While allopathic medicine may have taken on the mantel of science, it is not exactly clear that this constituted a superior medical method; certainly, at the time, there was no serious advantage to their scientific medical method because treatments for the most part were similar to those of the empirics who the allopathic physicians so railed against. In the early 1800s, the number of homeopathic practitioners nearly equaled those of allopathic physicians. The key

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to undoing the political victory by the empirics in the United States would come from educational reforms consequent to the 1910 Flexner Report to the Carnegie Foundation (Flexner, 1910). The report presented on the status of medical education in North America and found medical education wanting. The consequence was a subsequent closing of nearly half the schools of medical education, and those that remained were predominantly allopathic medical schools. The remaining schools affiliated with universities, primarily for financial reasons and attempting to cash in on the largess of the Gilded Age. Medical schools were to control clinical instruction in hospitals and strengthen state licensure requirements. Many argued that the Flexner findings were preordained as there was a strong move among allopathic medical schools to model the German schools of the 1800s, probably in no small manner related to the remarkable advances in the German histopathological understanding of disease. It probably was not coincidental that 11 of 16 presidents of the AMA between 1897 and 1912 had foreign medical training, of whom nine were from Germany; furthermore, none was a full-​ time clinician. Other recommendations included (1)  the faculty of the medical schools be scientists engaged in research and not just clinicians and (2) that they were to be paid a salary rather than patient-​derived reimbursements. Sir William Osler, a preeminent allopathic physician at the time, objected to both. Osler’s preference was teaching the student at the patient’s bedside instead of in a laboratory (Bliss, 1999). Osler was not anti-​science. Indeed, Osler made important contributions to medical science, particularly clinical–​pathological correlations. Importantly, the ability to translate observable patient phenomena into underlying pathology was a great boost to allopathic medicine even if it did not translate into different treatments. It can be argued that the tension between Osler and the proponents of the Flexner Report was never resolved but rather a truce declared. The predominant structure of medical school curricula even to this day is a division into the preclinical years, overseen predominantly by faculty with PhDs, after which the medical student is handed over to a physician faculty for the last 2 years of training. In my experience, most medical students view the first 2 years as not particularly helpful to how they see themselves practicing medicine and more as “dues paying.” These medical students are not anti-​science but may see science differently. Their view of “science” in the first 2  years is “science as experimental methods,” a position echoed by Martin Rees, president of the Royal Society (2005–​2010) who wrote “Armchair theory alone cannot achieve much. We are no wiser than Aristotle was. It is the technical advances that have enabled astronomers to probe immense distances and to track the evolutionary story back before our solar system formed” (Rees, 2010, pp. 467–​485). It is likely that many medical students probably do not think that the intricacies of polymerase

Evolution of Medical Reasoning

chain reactions or Western blots are particularly relevant to the practice of medicine, a sentiment Sir William Osler likely would agree with. The question becomes whether there are two sciences:  one that medical students and Osler would value, and one where the students and Osler decline. Interestingly, the Medical College Admission Test continues to feel that a knowledge of experimental techniques, such as Western blots, still is important to prospective physicians.

A Different Notion of Science While allopathic physicians claimed science, by contrast in many respects, the empirics more resemble the current concept of science—​that is, observation and experimentation (this will be seen as the Baconian notion of science)—​ than did the allopathic physicians who claim to be scientific. Allopathic medicine would come to prove its validity and utility over the empirics. However, the subsequent validation of allopathic medicine does not justify their epistemic position at the time of the debates. One could argue that the Baconian notion of science would resonate better with the empirics, at least in the sense that Baconian science would resist any notion of authority in scientific general principles and that every notion had to be subjected to the observations made in scientific experimentation. Indeed, the Royal Society of London was the height of Baconian science, and its motto encouraged members to “take nobody’s word for it.” This issue will come up again in evidenced-​based medicine, particularly as it became synonymous with randomized controlled trials, as discussed later. The identification of Baconian science suggests that there is an alternative science. One alternative is called Cartesian science. Rationalism and deduction are to Cartesian science as experimentation, observation, and induction are to Baconian science. An example of Baconian and Cartesian science can be demonstrated by contrasting Kepler’s and Newton’s approaches to the scientific knowledge of the elliptical orbits of the moon and planets. Utilizing an extensive database of astronomical observations left to him by Tycho Brahe, Kepler (1571–​1630) determined empirically that an ellipse best fit the data. Newton (1643–​1727), however, answering Edmund Haley’s question about how Newton knew the orbits were elliptical, replied that he had derived it mathematically with the use of his newly invented calculus. Kepler’s work would resonate more with Baconian science, while Newton’s work on elliptical orbits resonated more with Cartesian science. Baconian and Cartesian science, taken to their logical extremes, both fail. As discussed previously in Chapter 4, deduction, as in Cartesian science, fails to provide new knowledge and, at best, merely discovers knowledge that is implicit in the premises and propositions. However, one can reason from

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principles very effectively, and it is not necessary to reprove by observation Newton’s laws of motion each time they are needed. Furthermore, induction, as in Baconian science, necessarily presupposes certain criteria by which sets of observations, thought relevant, are assembled, the A  Priori Problem of Induction (Chapter 5).

7

Variability Versus Diversity in Variety THE EPISTEMIC CONUNDRUM AND RESPONSES

Is medicine an art or a science? Quoting Sir William Osler, a great physician, “If it were not for the great variability among individuals, medicine might as well be a science and not an art” (Sir William Osler, The Principles and Practice of Medicine, 1892, 9th edition, D. Appleton and Company, 1921, New York and London)

STUDENT:

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It is difficult to know whether the comment by the paragon of modern medicine, Sir William Osler, represents a hope or a lament. Which was preferable, medicine as an art or as a science? Osler lived in a tumultuous time when contrasting and contesting schools of medicine were vying for dominance (Chapter 6). At one end of the spectrum were the empirics, of which homeopathy was a dominant exemplar. The empirics held that a reductionist science was not possible and that medicine essentially is an art. At the other end were the rationalists (also called allopathic or regular physicians), who were the forerunners of modern allopathic medical and osteopathic doctors. Rationalists held to a reductionist medical science and held that their practice was scientific.

Variability Versus Diversity First, some discussion of the reason behind “variability,” the term used by Sir William Osler. The term “variability,” as used here, will be taken as meaning “variety.” This is to differentiate between “variability” and “diversity,” both of which are subsumed under variety but with different connotations. Variability between specific phenomena implies some relationship among the individual phenomenon; diversity implies none or at least less. Variability will refer to the notion of an archetypical or canonical form, such as the central tendency in a statistical distribution, where each instantiation represents a variation on the

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archetypical or canonical form. Diversity would be construed as differences between instantiations without an archetypical or canonical form. In diversity, each instantiation is taken as de novo. Osler likely would have agreed. Perhaps one of the most pressing epistemic issues confronting medicine today is evidence-​based medicine. Central to evidence-​based medicine is variability, not diversity. While early forms of evidence-​based medicine were fairly liberal in what constituted evidence, such as case studies and case reports of individual patients and expert consensus, the current prejudice is that only randomized controlled trials constitute legitimate evidence that should adjudicate questions of medical practice. For example, the mean (or median) effect of treatment X on disease A on a sample of patients is taken to be the real treatment effect. Any other effects, such as replication studies, are just variations on the real treatment effect. While this may seem relatively unproblematic, the question then becomes: What is the real effect held synonymous with the mean? Does that mean actually exist? If it does not exist, then the mean is taken as some sort of abstraction, perhaps analogous to the philosophical notion of a form. In Aristotelian philosophy, a substance is a combination of matter (material) and form. For example, a house is a form that can be constructed of any manner of matter, bricks, twigs, or stone. These issues are reviewed in greater detail in Chapter 8. The mean can be seen as a purely factitious construct. For example, one could argue that the average height of a North American male is 5 feet 10  inches; however, in any particular sample of adult men, an observer may find no one at the height of 5 feet 10 inches. Putting this in philosophical terms appropriate to the discussions in the 1600s and 1700s, one could argue that the World Soul dictates that the average height of an American male will be 5 feet 10 inches. However, it is the imperfections of the measurement because of accidents or subordinate forms (other types of souls) that some men are shorter while others are taller than 5 feet 10 inches. The problem is even more pernicious. Assume that there are a variety of cofactors, such as comorbidities, that influence the response to treatment X. Consider a study of a drug used to prevent stroke. In this case, a group of persons at risk for stroke are treated with either X or a placebo. A mean stroke reduction of 8% was found in those treated with X compared to a 2% reduction in those treated with a placebo. However, it is recognized that multiple risk factors may influence the risk for stroke and therefore may affect treatment X. For example, patients with high blood pressure, diabetes, high cholesterol, or a family history of stroke may be at more risk for stroke than others without those risk factors. In order to account for these risk factors, subjects in the clinical trials are counterbalanced by a random assignment of subjects to either the treatment or the placebo group. Counterbalancing assumes that an equal number of subjects

Variability Versus Diversity in Variety

randomized to the two groups will have the various risk factors compared to an equal number of persons assigned to the placebo group with those risk factors. The presumption is that the shared exposure to risk factors in the treatment group and the placebo group “counterbalances” the risk factors. The problem is that the means of stroke reduction in the treatment and placebo groups now reflect some combination of risk factors. The mean becomes the equivalent of a philosophical form and is accorded a higher epistemic and ontological status. The mean is the reality, and taking the average is the epistemic route to knowing the reality. However, the individual person either has or does not have hypertension, diabetes, or a family history of stroke. In this sense, the “mean subject” is very different from any individual. But by allowing the mean to have a higher epistemic and ontological status, the mean becomes the measure of any individual subject, and differences between any individuals are accidental or incidental and hence less relevant. This notion is the l’homme moyen, the average man, discussed in greater detail in Chapter 8. Since the development of modern statistics, there has been tension between what measure is most representative of the state or condition—​the central tendency or the variance (Gigerenzer et al., 1989). For example, two populations may have the exact same mean response to treatment, but how the individual patients in those groups respond may be very different (Chapter 8). One population may be skewed to the left such that the overall treatment effect is less for the majority of the subjects. The other group may have a significant shift to the right, suggesting that more subjects experience a greater treatment effect. However, both may have the exact same mean effect. The tension between central tendency and variability (variance) did not arise from the consternations of statisticians. The tension is not simply the mathematics by which the central tendency or variance is calculated, which could be viewed as an epistemic consideration. Rather, it lies in the ontological connotations that somehow “real” phenomena are best reflected in the central tendency. In his book, Stephen M. Stigler writes The first pillar I will call Aggregation, although it could be just as well be given the 19th century name, “The Combination of Observations,” or even reduced to the simplest example, taking a mean. Those simple names are misleading, and in that I referred to an idea that is now old but was truly revolutionary in an earlier day—​and it still is so today, whenever it reaches into a new era of application. How is it revolutionary? By stipulating that, a given number of observations, you can actually gain information by throwing information away! . . . The details of the individual observations had to be, in effect, erased to reveal a better understanding that any single observation could on its own. (Stigler, 2016, pp. 3–​4) The problematic nature of taking the central tendency (or summary measure of the aggregate in Stigler’s terms) is noted by Stigler’s quote of Francis Galton

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(1883, p. 350): “No statistician dreams of combining objects of the same generic group that do not cluster towards a common center; no more should we attempt to compose generic portraits out of heterogeneous elements, for if we do so the result is monstrous and meaningless.” This question begs that the prior selection process drives the result rather than the observations and is an example of the A Priori Problem of Induction (Chapter 5). Focusing on diversity, empiric medicine was and is very different from Rationalist/​allopathic medicine. Empirics saw the symptoms and signs of illness as manifestations of the body resisting illness. Empirics focused on the patient’s “peculiar” symptoms while ignoring the commonalities with other patients, analogous to focusing on the variance rather than the central tendency. The allopathic focus was the converse. Empirics embraced the differences as diversity in a strong ontological and phenomenological sense. That ontological notion necessitated corresponding metaphysics that manifest in the treatments offered. Rationalists held a reductionist scheme, whereby a few principles were sufficient to explicate the differences, and thus differences become variability where specific instantiations are combinatorial variations.

The Human Epistemic Condition This presentation centers on the distinctions between variability and diversity in natural philosophy and science, particularly medical science. In many ways, it is an epistemic device in which the metaphysical predisposition is to invoke variability or diversity in the absence of compelling evidence to choose one or the other. In medicine, this epistemic condition resulted in a relatively sharp distinction between the empiric/​irregular physicians and the rationalist/​ allopathic/​regular physicians. It was not until the early twentieth century that evidence finally favored rationalist/​allopathic/​regular medicine. This same epistemic condition is also evident in the transition from natural philosophers to modern scientists, particularly experimentalists. The epistemic condition and the choices it requires are seen in many other areas of science. For example, neuroscience ultimately seeks to understand brain/​mind function in the patterns of interactions of neurons. Thus, the expectation is that there is a one-​to-​one correspondence between specific brain/​mind functions and a specific anatomical/​physiological organization of neurons. Furthermore, there is the expectation that the specific anatomical/​ physiological organizations of neurons will be found in all humans who demonstrate the same specific brain/​mind functions. The current notion of a modular brain argues that many specific anatomical/​physiological organizations share the same general architectural principles and what differs are the initial conditions. In Galenic medicine, it is the imbalance among the same humors but in different organs that produces the specific disease. The variety of specific

Variability Versus Diversity in Variety

brain/​mind operations and anatomical/​physiological architectures represent variations on an economical set of principles—​variability rather than diversity. Willard Van Orman Quine (1908–​2000) pointed out that no two brains are exactly the same and, consequently, that any specific brain/​mind function cannot be represented in any specific anatomical/​physiological architecture. He makes the analogy to elephantine forms in topiary, in which allowably identical elephants can be carved from bushes whose patterns of twigs and leaves are not the same. In that case, the anatomical/​physiological architecture represents diversity rather than variability. To argue the latter, there would have to be some anatomical/​physiological architecture form that is archetypical or canonical and of which any particular anatomical/​physiological architecture is a variant. But how to identify or demonstrate that form is the critical question. That concern spawned the philosophical notion of functionalism, which has yet to find its footing in the neuroscience of neurons. The epistemic condition is even more basic or fundamental. In physical systems, there is a significant challenge to differentiate what is random behavior from what is determinant behavior. One could hold that random phenomena are akin to diversity, while determinant behavior is more akin to variability, where the underlying physics are an economical set of principles that are far less in number than the phenomena to be explained. Chaos and complexity theories have the potential to explicate these issues, as what previously may have been thought to be random actually may be determinant, even though the specific outcomes of such determinant systems cannot be predicted (Chapter 13).

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The Meaninglessness of the Mean

What is the effect of treatment X on disease Y? PROFESSOR: It resulted in a mean of 28% improvement. MEDICAL RESIDENT: So, my patient should get 28% better? PROFESSOR: That’s right. MEDICAL RESIDENT: Then why isn’t my female adult patient 5 feet 4 inches tall, which is the mean height of adult women in North America? MEDICAL RESIDENT:

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The professor may not have meant that the patient with disease Y would actually experience exactly 28% improvement with treatment X. She probably realized that although there is a range of treatment responses, the mean or central tendency was 28%. She may not have realized that the mean of 28% may not have been the most frequent response of any of the patients studied (the mode), and more than half may have a greater (or lesser) benefit if the distribution is skewed. In fact, it may even be that no patient in the clinical trial actually had a response of 28%. But then on what grounds does the professor assert the patient will have a 28% improvement and, consequently, how can the patient give informed consent to the treatment? The difficulty arises when a medical resident, obtaining informed consent, tells a patient that she is likely to get 28% better—​the validity of the estimate remains in question. If invalid, the patient received inaccurate information and cannot give informed consent. A clinician applying treatment X to a patient, technically, may be guilty of battering the patient, a criminal offense. No judge or jury likely would find the clinician guilty, but the epistemic question remains. Chapter 7 addressed the epistemic conundrum of variety as variability or diversity. One response to the conundrum was to assume variability and resolve the conundrum by selecting the central tendency as the ontological reality. This chapter explores the medical implications of such a choice and revisits variability as more informative of patient decisions.

The Meaninglessness of the Mean 16

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Distribution of the percentage of improvement, shown in gray, in a hypothetical case of treatment X. The height of each gray bar represents the number of subjects (frequency) whose percentage of improvement fell within the bin, which is 5% wide. Because the distribution approximates uniform distribution, all percentages of improvement are equally probable. The mean of the percentages of improvement is 28%, and the range is 0–​58%. Also shown by the orange line is the cumulative percentage function (reflecting probability), which may be thought of as the frequency (or probability) of an observation (percentage of improvement) whose value is less than a target or cutoff value. For example, the frequency or probability of a target or cutoff value being less than 28% would be 50% (or 0.5).

Consider the estimate of a response that is drawn from a sample of patients who have received treatment X. Figure 8.1 shows the distribution of the response ranging from 0% to 56% and whose mean is 28%. The distribution approximates a uniform distribution. Thus, it is just as likely for the patient to see 0% improvement as it is for her to see a 28% or 56% improvement. The patient truly does enjoy a 50% chance of seeing at least a 28% improvement, but, according to the clinical trial, she enjoys no chance of seeing a response greater than 58%. Consider the distribution of the individual responses shown in Figure 8.2. This mean response is 28% with a range of 28% to 28%. In this case it is absolutely clear that the patient may expect an improvement of 28%. This type of distribution is highly unlikely; the distribution shown in Figure 8.3 is more likely. It has a mean of 28% and a standard deviation of 5%. According to this distribution, the patient may have a 95% chance of seeing an improvement between 18% and 38% and a 65% chance of seeing a response between 23% and 33%.

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Distribution of the percentage of improvement, shown in gray, in a hypothetical case of treatment X. The height of each gray bar represents the number of subjects (frequency) whose percentage of improvement fell within the bin, which is 5% wide. The mean of the percent improvements is 28%, and the range is 28–​28%. Also shown by the orange line is the cumulative percentage function (reflecting probability), which may be thought of as the frequency (or probability) of an observation (percentage of improvement) whose value is less than a target or cutoff value.

It is not unreasonable to imagine a situation in which a patient indicates that only an improvement of at least 38% is an acceptable chance of benefit in view of the risks after the clinician provided the patient or her legal representative with real-​world descriptions of patients’ function at various levels of improvement. An improvement of 28%, for example, may mean greater comfort but no greater independence. Improvement of 38%, however, may bring greater independence. The question becomes whether simply gaining greater comfort is worth the risk or whether greater independence is the only acceptable outcome. In a case represented in Figure 8.2, the patient may forgo treatment because she enjoys no chance of seeing a 38% improvement. Yet, in a case represented in Figure 8.4, the patient enjoys a 16% chance of seeing at least a 38% improvement, which may or may not be acceptable. Finally, in a case represented in Figure 8.1, the patient enjoys a 33% chance of seeing at least a 38% improvement. Any distribution (also known as a probability density function) may be associated with a cumulative probability function, which may be interpreted as the probability of any observation that is less than the index observation or value. Were a patient to know the cumulative probability function, she could determine the probability of a minimally acceptable benefit.

The Meaninglessness of the Mean 35

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Distribution of the percentage of improvement, shown in gray, in a hypothetical case of treatment X. The height of each gray bar represents the number of subjects (frequency) whose percentage of improvement fell within the bin, which is 5% wide. The distribution is normal (Gaussian). The mean of all percentages of improvement is 28%, the range is 0–​58%, and the standard deviation is 5%. Also shown by the orange line is the cumulative percentage function (reflects probability), which may be thought of as the frequency (or probability) of an observation (percentage of improvement) whose value is less than a target or cutoff value. For example, the frequency or probability of a target or cutoff value less than 28% would be 50% (or 0.5).

Unfortunately, much of the medical literature consulted by clinicians reports only means—​a tendency that renders those reports useless for the most part. Some investigators publish standard deviations along with means. Reporting both allows these investigators to translate the target value—​the minimum percentage of improvement acceptable to a probability, for example—​by converting the target value to a z score. The probability of achieving at least a target value of 35% improvement may be determined by calculating the z score, which is the difference between the target or cutoff value minus the mean value divided by the standard deviation. In the case represented in Figure 8.5, the z score is 0.7, indicating the area to the left (toward from the mean) of the target value by the use of a one-​tailed test that contains 75% of the observations. The patient wants to know the probability of her seeing at least a 35% improvement, which is reflected in the area beneath the curve to the right of the cutoff and is 1 minus ​75%, or 25%. Unfortunately, the method just given is only valid if a normal, or Gaussian, distribution is assumed. Figure 8.6 shows two distributions of equal means and standard deviations but unequal cumulative probability functions. Despite

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Distribution of the percentage of improvement, shown in gray, in a hypothetical case of treatment X. The height of each gray bar represents the number of subjects (frequency) whose percentage of improvement fell within the bin, which is 5% wide. The distribution is normal (Gaussian). The mean of all percentages of improvement is 28%, the range is 0–​58%, and the standard deviation is 10%. Also shown by the orange line is the cumulative percentage function (reflects probability), which may be thought of as the frequency (or probability) of an observation (percentage of improvement) whose value is less than a target or cutoff value. The frequency or probability of a target or cutoff value less than 28%, for example, is 50% (or 0.5).

equal means and standard deviations, the probabilities of reaching the same target value are different.

Medians and Quartiles Means as a measure of central tendency may not be the best descriptor in cases of highly skewed (non-​normal or non-​Gaussian) distributions. In such cases, it may be more fitting to use the most frequent data value, the mode or the median, in place of the mean. Similarly, the standard deviation may not be appropriate for highly non-​normal distributions; the quartiles may give a better indication of the distribution. In the distribution shown in Figure 8.7, for example, the median, which is the data point where half the values are greater than the median and half are far less, is 12%; the mean is 18.8%. The difference between the median and the mean often grants insight into the degree to which the data distribution is skewed. Also, both the mean and the median values are not the

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Distributions of the percentage of improvement, shown in gray, in a hypothetical case of treatment X. The height of each gray bar represents the number of subjects (frequency) whose percentage of improvement fell within the bin, which is 5% wide. The two distributions have equal means and standard deviations. Also shown by the orange lines are the cumulative percentage functions (reflecting probability), which may be thought of as the frequency (or probability) of an observation (percent of improvement) whose value is less than a target or cutoff value. The cumulative percentage (probability) functions are quite different. The patient would have very different probabilities of achieving the target or desired degree of improvement depending on which distribution is applicable.

Medical Reasoning Mean Mean + STD

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most frequent improvement (mode) in the sample, which is 10% improvement. The standard deviation of the distribution in Figure 8.7 is 13.8%. The 25th quartile is the data value for which 25% of all data is less than the 25% quartile value. Similarly, the 75% quartile is the data value for which 75% of the data points are less than the 75% quartile value. The 50% percentile equals the median. A patient’s probability of achieving a minimally acceptable result is problematic unless the cutoff lies at the quartiles because the shape of the cumulative probability function between quartiles cannot be assumed.

Cumulative Percentage (Probability) Function The question is how can clinicians and scientists report the cumulative percentage (probability) function in a form useful to other clinicians? The scientist may model a cumulative probability. Such probability functions typically approximate a sigmoid function, expressed in the following equation: Y (x) =

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The Meaninglessness of the Mean

where B and M are curve-​fitting variables and x is the measure of interest, such as the percentage of improvement. Thus, the cumulative probability function may be fully described by two variables, B and M. Also, adequacy (hence internal certainty) of fit of the calculated sigmoid function or model to actual data can be determined. Solving for Y(x), where x is the acceptable probability cutoff, results in a value; namely, the probability of any one patient experiencing a percentage of improvement that is less than the cutoff. Interpretation may be made easier by subtracting the value of Y(x) from 1 to give the probability of obtaining the acceptable cutoff value, or 1 − Y(x). The cumulative percentage (probability) function may aid a patient or her legal representative in weighing a treatment’s risks against its potential benefits. However, because the distributions in the examples given previously reflect the experience of the entire sample, extrapolation from sample characteristics to an individual patient is highly problematic. Furthermore, there is the question of whether the sample is representative of the population that would include the patient risking, if there is doubt the Fallacy of Four Terms would be at risk (Chapter 5).

The Metaphysical Notion of the Mean (and Median) The significance of the central tendency—​mean, median, or mode—​is not limited to its arithmetic function, which is only internal to the data. The central tendency also imputes an ontological status as representing the true nature of a statistical sample with the expectation that it is representative of the population. The alternative to imputing an ontological status to the central tendency is to regard it as an artifact of the distributions, and it is the distributions that reflect the true nature of reality. The determination of which measure truly reflects ontology has been debated since the advent of statistics (Gigerenzer et  al., 1989). This debate continues to influence medical decisions. While the central tendency may also not be a matter of fact (ontology), it often is used as a quasi-​fact—​an artifice that carries the force of a fact. It is important to understand the nature of the variance. One source is the biology of the subject, to be discussed in more detail later. Another is the variability of the measure or instrumental variability. For example, an object weighed a number of times will likely weigh differently each time, despite the fact that it underwent no change of nature between each weighing. The inverse problem (Chapter  2) holds that the variance of any study, experiment A, for example, is the sum of all the sources of variability and diversity, including biological and instrumental variability. However, it is impossible from the variance of the study to determine the contribution of any source of variance. Note

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that the variance of a method, procedure, or device used in experiment A can be extrapolated from experiment B but this risks the Fallacy of Four Terms. Also note that experiment B cannot be the same as experiment A to avoid the fallacy because then experiment B would suffer from the same compounding confounds of multiple sources of instrumental variance. Rather, Mill’s joint method of agreement and difference would be needed, thus involving a number of different experiments (Chapter 5). Given the metaphysical privileged notion of central tendency over variance, many clinicians attribute variance in the outcomes to instrumental variance (thereby maintaining a faith in the central tendency):  those that cause biological variability. “[C]‌ertain physicians seem to reason as if exceptions were necessary; they seem to believe that a vital force exists which can arbitrarily prevent things from always happening alike; so that exceptions would result directly from the action of mysterious vital forces,” wrote the highly regarded early scientist Claude Bernard (1813–​1878). “Now this cannot be the case; what we now call an exception is a phenomena, one or more of whose conditions are unknown; if the conditions of the phenomena of which we speak were known and determined, there would be no further exceptions, medicine would be free from them as is any other” (Bernard, 1865, p. 70). For his part, Claude Bernard was hostile to the idea that a statistical mean was appropriate. In his time, the concept of l’homme moyen or “the average man” reigned, an idea which rested on the belief that the statistical mean captured the essence of humanity sufficiently. “If we collect a man’s urine during twenty-​four hours and mix all this urine to analyze the average, we get an analysis of a urine which simply does not exist; for urine, when fasting, is different from urine during digestion,” Bernard wrote. “A startling instance of this kind was invented by a physiologist who took urine from a railroad station urinal where people of all nations passed, and who believed he could thus present an analysis of the average European urine!” (Bernard, 1865). If one held the opinion that l’homme moyen or “the average man” was true, then every adult woman is 5 feet 4 inches and that why any one woman appears otherwise is due to imperfections of the measuring device. What is the nature of the instrumental variance or “noise,” or, importantly, what is it presumed to be in medical knowledge? For many, instrumental variance or noise has a symmetric distribution around a mean of zero. Repeated measures would thus result in a summing of instrumental variations and noise. This summing, in turn, would cancel instrumental noise yet leave the mean unaffected. The standard deviation or other similar variance, however, would nonetheless be affected by the instrumental and universal noise. However, at least in some circumstances, particularly in complex systems involving multiple and repetitious processes, this presumption is not likely.

The Meaninglessness of the Mean

Different Types of Means Attesting to the metaphysical notions of the central tendency are the several competing notions of the central tendency. As already discussed, there is the mean, median, and mode. If the central tendency reflects the true nature of reality, how can all three be correct? Furthermore, there are many forms of the mean, of which three are shown here: Arithmetic mean (average): x=

x + x2 + … + xn 1 n xi = 1 ∑ n i =1 n

Geometric mean: 1

 n n x =  ∏xi  = n x1 * x2 * …* xn  i =1 

Quadratic mean: x=2

x12 + x22 + … + xn2 n

Which form of the mean is determined by the intent of the analysis? The geometric mean is often used to establish a central tendency where an entity, for example, has several measures, each over a different range. The arithmetic mean would give greater influence to the value in the larger range. Thus, the mean is an epistemic device directed by the ontological intentions of the clinician and scientist.

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The Value of Statistical and Logical Thinking

Look at the magnetic resonance imaging (MRI) scan of the brain in this patient who has not had a good response to deep brain stimulation (DBS). I think the DBS lead (the collection of electrical contacts for providing electrical stimulation to the brain to treat neurological and psychiatric disorders) is not in a good position, which is why the patient is not doing as well as she should be. PHYSICIAN 2: I don’t think the position is the problem. PHYSICIAN 1: How do you know? PHYSICIAN 2: Intuition. PHYSICIAN 1: Seriously, why do you think the position of the lead is not a problem? PHYSICIAN 2: Intuition. PHYSICIAN 1:

To be sure, many authors have touted the value of intuition, which will be defined as a claim not directly evident (Groopman, 2007; discussed in detail in Chapter 19). Yet the question is, “What is the nature of intuition?” Doesn’t the clinician have a responsibility to justify its use or at least assure it is being used judiciously? Otherwise, doesn’t the invocation of “intuition” act as a “get out of jail free” card for physicians’ accountability? Physician 2’s use of intuition clearly is an abuse of the discretion allowed physicians. Its use by Physician 2 allowed no critical discussion and thus is a form of solipsism (Chapter 18). More critical to this book is whether intuition represents a unique mode of reasoning. As will be seen, the issue centers on the role of intuition in suggesting hypotheses to be tested, an epistemic function. Or does intuition have some ontological relevance? Often, the epistemic and ontological senses are conflated. In the sense used by Physician 2, intuition appears to be a means to assert reality, the position of the deep brain stimulation (DBS) lead. 108

The Value of Statistical and Logical Thinking

Wired for Intuition? The role of intuition in medical reasoning is addressed in greater detail in Chapter 19. Clearly, there are clinicians who appear to make valid inferences seemingly out of nowhere. In other words, intuitions do not appear to follow from a logical chain of reasoning where each step can be explicated. In this sense, the best definition of medical intuition is what intuition is not. Plato made the distinction between perceptions that are “given” and those that must be “intellected.” Those given appear unmediated, a spot of red on a canvas of white. Images that produce multiple perceptions likely are intellected in that some mediating process is brought to bear. Medical intuition may be like the given. The Necker cube is an example of a perception that is intellected because the actual visual stimuli consist of more than one perception (Figure 9.1). The two-​dimensional “wire” image is seen as a three-​dimensional figure. The shaded area at one time is seen in the front, only later to be seen in the back of the three-​dimensional see-​through box. The perception of jumping back and forth would appear to be effortless. Yet there is nothing in the image itself to determine whether the shaded area should be seen as the front or the back. The retinal stimulation is exactly the same whether the shaded face is at the front or at the back. The image in Figure 9.2 can be seen as either a pile of vegetables or a human-​like head. However, the fact that the image seems to jump spontaneously from a human-​like head to a pile of vegetables would seem to be unmediated.

FIGURE 9.1 

Example of a perception—​whether the shaded area is in the front or back of this three-​dimensional cube—​that requires intellect. There is nothing in the image itself, and thus nothing in the sensations following from activations of the retina, that determines the location of the shaded surface. As in Plato, the image contains its opposites, the shaded surface on the front or on the back. Such a perception requires operation of the mind/​nervous system.

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FIGURE 9.2 

Giuseppe Arcimboldo in 1573.

The image must be searched for, but, after a time, the horses are perceived, consistent with Plato’s notion of requiring it to be intellected. The necessity of a visual search would make it seem that visualizing the horses was not intuitive. Afterward, it takes practically no searching to see the horses, and seemingly then it is not intellected but rather appears as intuitive. Yet the visual stimuli are exactly the same. Perhaps perception of the horses is now unmediated and a type of intuition, whereas, prior to perception, the horses had to be intellected first to see them. If medical intuition is similar, then physicians do not start from pure intuition but rather from an experience that must be considered (intellected). Thus, intuition is a logic that is made implicit. It might well be that humans are “wired” psychologically or neurophysiologically in such a way that resonates with these intuitions. Certainly, this notion has been fertile grounds for the Gestalt psychological understanding of perception. Studies in patients with congenital cataracts who first regain their vision tend to see optical illusions that suggest a preexisting disposition to see in a three-​dimensional manner. So, too, it may be the case that humans are wired to approach medical decision-​making in a Gestalt-​like intuitive manner. As a consequence, psychological analyses may well be at a very good level of abstraction to understand these forms of medical reasoning. There may be a deeper level of analysis. Humans may have biologically evolved to respond to the inherent three-​dimensional structure of the world. Extrapolating that concept to evolutionary epistemology and evolutionary

The Value of Statistical and Logical Thinking

logic provides a means for explicating these intuitions. Immanuel Kant’s notion of the Categories, particularly his notion of the pure forms of space and time, provides a basis for understanding. Kant (1781) called these synthetic a priori; that is, not independent of humans (synthetic) but given prior to the individual human’s experience (a priori). Kant might hold that humans are “wired” to perceive space and time as humans typically do even if the ontological status of space and time was debated. Evolutionary logic responds to the ontology of the relevant universe.

Statistical and Epistemic Thinking Whatever intuition is, it is hard to totally discount its role in medical decision-​ making and knowledge generation. Intuition is discussed in greater detail in Chapter  19. However, it is clear that it plays a role in hypothesis generation through metaphor and the Fallacy of Pseudotransitivity (discussed in greater detail in Chapter 10). The risks of error, as well as potential new knowledge, can be assessed in at least a qualitative sense through epistemic risk (Chapter 2). Even though probability and statistics may be difficult to apply in a specific manner to the operations of intuition, at the very least, there are valuable lessons of logic, probability, and statistics that can and should be brought to bear on any intuition or proceedings from intuition. These are reviewed here. Reticence to testimonials. Most clinicians are unlikely to accept the testimonial of one or a few. This is a tacit recognition of the inverse problem. For example, consistent with the inverse problem, multiple reasons may exist for improvement in disease A with treatment X. Any single patient’s improvement may be due to any one or more of these factors, but it is difficult, in the absence of quantitative data, to infer which of the causes is most likely. Operationally, it may be “intuitive” that a clinician would not accept the testimony of a single patient (n of 1) even if that clinician does not fully appreciate, in a quantitative sense, the notion of statistical power and sample size. The problem then becomes one of determining how many testimonials, such as in case series, become convincing. The problem is compounded by the salience of each case, with those most recent, dramatic, or resulting from the clinician’s involvement skewing what would be reasonable to conclude. This is not to say that such an experience, such as in case series, is inappropriate, but rather that caution should be actively applied, as will be discussed later. Whether the intuition of reticence to testimonials was innate or learned by imitation or explication is an open question. Appreciation of the complexity, hence variability, of disease. One often hears clinicians state “it is just too simple for such and such to be the case.” How is it that wearing a copper bracelet improves arthritis? This question is an example of the Principle of Causational Synonymy dating back to the ancient Greeks,

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particularly Aristotle. The principle holds that the mechanisms by which a cause creates an effect must be the same as the mechanism that is in the effect. Allopathic medicine requires a causal chain of events, from the fundamental or constituent elements to the whole phenomenon. The statement that “it is just too simple for such and such to be the case” is recognition that the epistemic requirement of an explicit complete causal chain has not been met. Application of the skeptical intuition just described can be very helpful, but only when applied judiciously. Aspirin was known to be an effective analgesic and antipyretic long before the casual chain was understood. Interestingly, evidence-​based medicine, particularly in the application of randomized control trials (RCTs), need not make any allusions to causal mechanisms. According to evidence-​based medicine, if a ground-​up kitchen sink statistically significantly improved disease A, then, all other things being equal, ground-​up kitchen sink should be used for the treatment of disease A, even if one has no idea how a ground-​up kitchen sink could cause an improvement in disease A. Perhaps the reticence to accept the absence of causality puts off clinicians and explains why the subsequent insistence by authorities of evidence-​based medicine gives the impression of an existing hegemony (Cartwright, 2007). The lack of applying such skepticism creates difficulties as well. Intuitive notions, particularly those related to cause and effect, are powerful and often resist considerable contrary evidence (Johnson-​Laird, 2008). The notion of certain diseases, such as Parkinson’s disease, as a consequence of the relative deficiencies of a neurotransmitter is an example: it does not comport with the complexity of the phenomena affected, such as the precise orchestration of the motor unit (combination of muscle fibers innervated by a single motor neuron in the brainstem and spinal cord) that occurs at a time scale of milliseconds. It is not surprising that pharmacologically administered dopamine or dopamine neuron transplants would fail to fully reverse the disabilities, which is evidence of the importance of the epistemic Principle of Causational Synonymy. Apprehension toward applying sample-​based inferences whole cloth to the in­ dividual patient. This is seen when clinicians temper recommendations from randomized clinical trials in the care of a specific patient. Often this apprehension is felt implicitly, suggesting that it is an intuition. Whether implicit or explicit, it reflects the epistemic a priori problem of induction contributing to a Fallacy of Four Terms. The clinician knows or suspects that the particular patient is different from the sample utilized in the RCT. Suspiciousness for bias. Contributing to the reluctance to apply results from clinical trials to individual patients is the suspicion that the sample of patients used in the clinical trial may have been biased. For example, studies done with young subjects may intuitively cause concern when an older patient is considered. Again, this reflects the nature of inferring from one group to another, even if the latter is only a single patient, which necessitates concerns for the Fallacy of Four Terms described earlier.

The Value of Statistical and Logical Thinking A

1

2

B

1

2

FIGURE 9.3 

Schematic representation of the effects of ergodicity. (A) The fish are distributed evenly in the lake—​high ergodicity. (B) The fish are concentrated under Angler 1. The experience of the two anglers in condition A will be the same and considered reproducible. Their experiences will be different under condition B, and the experiments will be considered irreproducible.

The problem of sample size is compounded by biased sampling. For example, case studies by physicians in major academic medical centers 30 years ago reported that multiple sclerosis incapacitated nearly every patient. However, the majority of patients seen by specialists were at major academic medical centers and generally very ill. Those patients doing well rarely went to specialists. A very different picture emerged when careful and systematic surveys reasonably approximating the population of concern were done. A very important assumption of ergodicity has to do with how data or observations are distributed (Figure 9.3). If data or observations are random, thus distributed evenly in space (a uniform random distribution), then the ergodicity is high, and one only needs to sample a relatively small volume of the space to obtain a representative sample, as would be the case for each angler sampling the fish in the lake in Figure 9.3A. If, however, the fish are distributed randomly but not evenly (for example, a Poisson distribution), the two anglers will have very different experiences. The result is that statistical inferences for angler 1 in Figure 9.3B will not be the same as for angler 2, even though the fish are distributed randomly in the lake.

Are Randomized Controlled Trials Privileged? Evidence-​based medicine has become synonymous with RCTs such that other sources of knowledge (e.g., case series) are discounted (Montgomery and Turkstra, 2003). But is this truly the case? Using randomly selected subjects from the population of concern to be included in the sample is another way to avoid bias. However, the effect of randomization also depends on the size of

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the sample relative to the population; consequently, there is only a quantitative difference between the randomized sample and the sample typically associated with case series. Most case series are retrospective and thus are at a greater risk for bias. One way to reduce sampling bias is to include every consecutive patient in the sample, which assumes the order in which patients present for examination is random—​or at least random enough. Complete consecutive sampling approaches a random selection with a large enough sample size. Again, it would appear that the difference between RCTs and case series is one of degree rather than kind; consequently, there is no a priori reason to necessarily discount knowledge gained from case series. The purpose here is not necessarily to defend case series but rather to “rehabilitate” case series based on the methods of RCTs to ensure generalizability and hence clinical utility. The reason that case series need “rehabilitation” is because they are the first and foremost source of knowledge for clinicians. The entire clinical training of pregraduate and postgraduate clinicians depends heavily on a sequence of experiences of cases both of their own and those based on the effect of their mentor’s experience as well.

Undersampled and Biased Experience and Induction As much of the education of clinicians is an apprenticeship based on a sequence of experiences, the nature of induction from that experience is critical. Inductions from experience provide the general premises that are used for abduction in medical decision-​making. As discussed in Chapter 5, abduction is the most frequent approach in diagnosis and treatment selection. It is the nature of clinical experience that drives the quality of the general principles used in abduction. If the clinical experience produces flawed gen­ eral principles, then diagnosis and treatment decisions are likely to be flawed. Considerable evidence shows that the nature of experience often creates flawed diagnoses. In a study of physicians in different medical subspecialties given a series of cases where the facts were somewhat ambiguous, each physician made a different diagnosis and each diagnosis was in the physician’s area of specialty. Consider the example of cervical dystonia. A  very large percentage of patients with cervical dystonia, which presents with persistent muscle activities producing contortions about the head and neck, are diagnosed as psychogenic. Their clinicians search in their minds for a general principle or disease that would explain the patient’s contortions, if that disease were present. In their clinical experience, it is highly likely that they have never seen a patient with cervical dystonia, as the estimated prevalence is 28–​183 cases per million of the population (Defazio et al., 2013) and, consequently, never induced a general principle to allow abduction to a diagnosis of cervical dystonia.

The Value of Statistical and Logical Thinking

The clinicians most likely have seen cases of presumed psychogenic disorders, which often present with bizarre symptoms and signs, with an estimated prevalence of psychogenic movement disorders of 4–​5% of patients seen—​much larger than the prevalence of cervical dystonia. Consequently, a very large percentage of clinicians induce that bizarre movements are seen in psychogenic patients. The general principle for abduction becomes if the patient has a psychogenic movement disorder, then the patient will have bizarre movements. In the case of patients with cervical dystonia, their movements could be considered by most to be bizarre. Hence is it not hard to understand why so many patients with cervical dystonia are initially misdiagnosed as having a psychogenic disorder. The question that rightly can be asked is “Why is the experience of the physician or healthcare professional so undersampled that they have never encountered a patient with cervical dystonia that was diagnosed correctly?” It would be thought that the evolution of medical education from lecture-​ based to problem-​or case-​based could be a corrective. Since the early 1900s, medical education typically proceeded from pathology first and then to clinical phenomenology, thus aggravating the problems of abduction described previously. Starting from clinical phenomenology, problem-​or cased-​based learning would appear to be relatively immune from the fallacies associated with abduction. However, in my experience, most problem-​or case-​based exercises start from the presentation of a single case or problem. There is a strong tendency to focus on a single diagnosis, thus risking the Fallacy of Limited Alternatives. An alternative approach is to present a simultaneous collection of cases that contrast on the dimensions relevant to the skill or knowledge to be gained. For example, when mentoring students to be able to diagnosis movement disorders, cases of Parkinson’s disease, essential tremor, tardive dyskinesia, weakness due to upper motor neuron dysfunction, weakness due to lower motor neuron dysfunction, cerebellar degeneration, peripheral neuropathy, and apraxia are given. The students compare and contrast the clinical phenomenology associated with each case to establish a means for the differential diagnosis, thereby avoiding the Fallacy of Limited Alternatives. Furthermore, differences in the clinical phenomenology can be related to the underlying pathophysiological mechanisms. There is the problem of sequestration of patients in postgraduate clinical training. For example, few postgraduate medicine programs require residents to rotate through the inpatient neurology service and even fewer through the outpatient neurology service, where most of the patients with cervical dystonia are seen. Furthermore, most patients with cervical dystonia, once identified as such, will be referred to the neurology clinic, and, again, the future primary care physician likely will never see a patient diagnosed with cervical dystonia. Instead, these patients often have an MRI scan, and the typical lack of any abnormality reinforces the faulty notion of a psychogenic cause. This procedural

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default tendency is even more disconcerting when the cost of an MRI scan of the brain is much more than a neurological consultation. Then there is the issue of recall bias. Unless scrupulous efforts to document and review one’s experience are made, only the most recent experiences or most dramatic are retained. Furthermore, the ability to recall an experience is related to the salience of the experience, which often does not correlate with the appropriateness of that experience to decision-​making. Consider the example of a physician treating a patient for strep throat with penicillin. The patient has an anaphylactic reaction. The physician may be more likely to prescribe erythromycin to the next patient he sees with strep throat even if the patient does not have a history of penicillin allergy and even though the last hundred previous patients did well on penicillin. Recall bias can be considered a form of biased sampling.

Non–​Evidence-​Based Medicine Methods of Medical Reasoning The early use of levodopa for treating Parkinson’s disease was not the result of some induction, deduction, or abduction specific to human Parkinson’s disease. The use of levodopa was derived from a metaphor relating reserpine-​treated rodents to the symptoms of humans with Parkinson’s disease—​an example of the Fallacy of Pseudotransitivity. It was only found subsequently that dopaminergic neurons in the substantia nigra pars compacta degenerated, resulting in a loss of dopamine. The mechanisms of action of levodopa—​replacing the lost dopamine in reserpinized rodents—​led to the use of levodopa in humans with Parkinson’s disease. The off-​label use of drugs, biologics, and devices also represent a non–​ evidence-​based medicine method; one example is the use of tetrabenazine for the treatment of tardive dyskinesia based on its use for involuntary movements associated with Huntington’s disease, for which RCTs have demonstrated evidence and the US Food and Drug Administration has approved, as described in Chapter 5. Sometimes the use of metaphor or the Fallacy of Pseudotransitivity can turn on a single word. For example, myasthenia gravis is thought to be an autoimmune disorder in which the patient generates antibodies against his or her own tissue. Consequently, plasmapheresis or plasma exchange was found effective in treating myasthenia gravis, where, presumably, the process rids the body of antibodies that are damaging the patient’s own tissues. The metaphor was constructed as plasmapheresis is to myasthenia gravis and myasthenia gravis is to other autoimmune disorders. Consequently, plasmapheresis is used to treat other autoimmune disorders, such as Goodpasture’s syndrome, Guillain–​Barré syndrome, lupus, and thrombocytopenic purpura. In these cases, the metaphor was effective, and the Fallacy of Pseudotransitivity was actually the logical

The Value of Statistical and Logical Thinking

Principle of Transitivity in disguise. The Principle of Transitivity would be that autoimmune disorders imply myasthenia gravis is true, myasthenia gravis implies plasmapheresis is true, and therefore autoimmune disorders imply plasmapheresis and is of the logical form if a implies b is and b implies c is true, then a implies c is true. However, the metaphor failed in the case of patients with multiple sclerosis despite evidence that multiple sclerosis is an autoimmune disorder (Gwathmey et al., 2014).

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The Centrality and Origins of Hypotheses

This patient moves and positions her head in a strange way. What is the diagnosis? ATTENDING PHYSICIAN: Her condition is psychogenic, not real. STUDENT: How do you know? ATTENDING PHYSICIAN: I have never seen any real disease produce these postures or movements. I have seen psychiatric disorders produce some weird things. STUDENT:

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Later examination by a movement disorders neurologist revealed that the patient had the undeniably real neurological disorder of cervical dystonia. Until such time as the patient received this definitive diagnosis, she endured a good deal of suffering, both physical as a consequence of her condition and emotional as a consequence of delays in treatment following from an incorrect earlier diagnosis of her condition as psychogenic. Effective treatment finally reached her in the form of botulinum toxin injections. Despite intuition, only rarely does a diagnosis spring fully formed from a clinician’s head, as Athena is said to have sprung from the head of Zeus. A clinician begins, rather, with an inkling that begins to take shape from the first contact with the patient. The inkling elaborates into a hypothesis, which becomes the premise of an abduction that subsequently shapes further evaluation. She thinks, implicitly or otherwise, if the patient has disease (premise a), then there should be symptoms (premise b), there should be signs (premise c), and there should be laboratory abnormalities (premise d). It is important to note that the progress of evaluation begins with the hypothesis that forms the premise of the abduction. The symptoms, signs, and laboratory findings solicited follow directly from the hypothesis or premise. This is clearly not an induction: a physician or healthcare professional does not routinely ask a patient about her sore throat or some other symptom, palpate her lymph nodes or look for some other sign, take her temperature, and then consider the possibility that she has strep throat. Where would the physician stop? This is the

The Centrality and Origins of Hypotheses

a priori problem of induction, there has to be some prior notion that limits the history-​taking and physical examination into something that is feasible yet with a low risk of false positives. As discussed in Chapter 5, abduction can be made more certain by changing the abduction to a deduction of the following form: If and only if a implies b, for If a implies b. However, it is critically important to note that the logical operator if and only if means that the differential diagnosis, or set of hypotheses regarding the condition, is exhaustive of all reasonable possibilities. The final diagnosis is only as good as the hypotheses entertained. The effort now is to understand the origins of hypotheses, what makes one hypothesis better than another, and which hypotheses should be entertained.

The Importance of Hypotheses The inverse problem means that the specific diagnosis cannot be inferred directly from the symptoms and signs that the patient presented with, making this problematic. Furthermore, the possible (note not probable) range of hypotheses may be enormous and require some prioritizing. While such prioritizing in science may be on the basis of probability, medical decisions require ethical considerations as well. In allopathic medicine, a necessary diagnosis is categorical and canonical in nature. However, due to variability, fitting an individual patient to a canonical category is often difficult at best. One could argue that optimal practice is to fit to the best approximation that results in a minimal residual (error). This may sound exacting, but often it is not, particularly when there are competing canonical categories. Often, hypotheses for the subsequent hypothetico-​ deductive approach are derived by metaphor, specifically the Fallacy of Pseudotransitivity. For example, my patient is to tremor and Parkinson’s disease is to tremor. In addition, my patient is to tremor as essential tremor is to tremor. Similar constructions can be made to cerebellar disorders, hyperthyroidism, sympathomimetic drug use, alcohol and drug withdrawal, and others. In each case, my patient is the target domain that gains credibility by the source domains, which include Parkinson’s disease, essential tremor, cerebellar disorders, hyperthyroidism, sympathomimetic drug use, alcohol and drug withdrawal, and others. Metaphors are discussed in detail in Chapter 13. The metaphors used to generate hypotheses for the differential diagnosis can be prioritized based on epistemic grounds, specifically the epistemic risk (Chapter  2). Epistemic distance, a component of epistemic risk, can be the prior probability that my patient has Parkinson’s disease or any of the others. Furthermore, an iterative process that alters the prior probabilities can reconfigure the differential diagnosis. For example, determining that the patient has

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not taken any sympathomimetic drugs increases the epistemic distance greatly, thereby creating a greater epistemic risk in diagnosing tremor secondary to sympathomimetic drug use. Determining whether the tremor is at rest or only with action further redefines the prior probabilities. The relevant prior probabilities are not the prevalence rates derived from actuarial studies of populations. Rather, it is the prior probabilities shaped by the patient’s unique situation and duration of symptoms, as highly acute life-​ threatening diagnoses have lower prior probabilities as the patient survives. Prior probabilities are altered by evaluations by prior clinicians. The prior probability of a structural lesion causing headaches is very low for patients seen by general practice physicians, often making imaging more likely to produce more false positives than true positives. However, patients referred to neurologists generally are screened in such a manner that prior probabilities of a structural lesion make subsequent imaging more productive. Prior probabilities should not be based on the personal experience of individual clinicians. The patient described at the beginning of this chapter is a case in point. Differential diagnoses are also influenced by ethical considerations. For example, it is exceedingly rare in modern times to encounter a patient with dementia secondary to a vitamin B12 deficiency. Nonetheless, a very large percentage of experts routinely test for vitamin B12 deficiency, if only because of the sense of moral tragedy of missing a potentially reversible cause of dementia and the simplicity and relatively low cost of a test for the condition. At some point, it is a societal question to determine how vigorously the differential diagnosis is to be pursued. Such considerations escape utilitarian solutions and thus turn on which moral theory is dominant: egalitarian, libertarian, or deontological (Kantian) (Beauchamp and Childress, 2013). There is a tendency to view these ethical questions as extra-​scientific or nonlogical. However, observations of the ethical actions of clinicians can serve for the induction, as in science, of general principles that allow for a hypothetico-​deductive approach to the ethical elements in medical decision-​ making (Chapter 1). The concept of common morality is constructed by an inductive analysis of ethical decisions made by reasonable persons (Beauchamp and Childress, 2013).

Origins of Hypotheses The preceding discussions of the importance of hypothesis and the Fallacy of Pseudotransitivity (metaphor) leave the source of the metaphors unanswered. Theories involve some set of principles; however, by definition, there are gaps and limits. If there were none, the body of principles would not be a theory but rather a set of laws. Whether such a set of wholly complete and self-​contained principles could exist such that they would not be theory is beyond the scope

The Centrality and Origins of Hypotheses Interpolation

Extrapolation Theory A

Theory A

Theory B

Theory B

Theory A

Theory A

Interpolated data Observed data

Extrapolated data Observed data

FIGURE 10.1 

Hypothetical experimental results demonstrating gaps between observed data. The scientist creates hypotheses about what data may be at a gap by interpolation or beyond the range of observed data by extrapolation. As can be seen, there are any number of relationships between the observed data represented by the broken line and representative of possible theories. As can be seen, the hypothesized or predicted data point differs depending on the choice of theory. Note that the theory choice does not come from the observed data and the metaphysical presupposition: Is a simple straight line (theory A) more likely given Occam’s Razor, or is the complex line (theory C) that appears to more closely follow the observed data better? What about some compromise (theory B)?

of this book, but, suffice to say, Gödel’s incompleteness theorems suggest that it is unlikely. Theories become the means to “fill in gaps” by interpolation and to exceed the limits by extrapolation. Interpolation and extrapolation within and from data within the theoretical context are sources of hypotheses (Figure 10.1).

Hypotheses and Postmodernism In the 1990s, there was an attack on the unique and privileged epistemology of science and thus on the veracity of its ontology by those called postmodernists; these debates were called the “science wars.” Although a heated debate then, it appears to have fizzled, with scientists largely coming to ignore the postmodernists. In a way, this was a shame because there were some good arguments that science and medicine could have benefited from if considered seriously. The point here is to consider how an understanding of hypothesis origination could resolve at least one dispute between modern scientists and clinicians and the postmodernists. Thomas Kuhn’s The Structure of Scientific Revolutions (1962) was a revolutionary account of scientific progress, although Friedrich Nietzsche (1844–​ 1900) provided some of the intellectual groundwork. Kuhn’s historiography and the subsequent philosophical extensions by the postmodernists, such as Feyerabend (1975), seemed to dislodge the traditional rational incremental

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view of scientific progress. Their history of science demonstrated the sociological, psychological, and political dimensions that impact scientific progress. It is striking that the term “scientist” was originally coined by William Whewell (1794–​1866) to describe one who does science, an idea that now is understood as science is what scientists do (Osbeck et al., 2011). Gone is any notion of an external reality amendable to a logical positivistic analysis. While it may be wishful thinking that science is value free and above politics and sociological and psychological influences, the shoe of history fits too well. A postmodernist approach was applied to science and the practice of medicine—​examples include Michel Foucault’s The Birth of the Clinic (1973). Scientists might disagree with the postmodernist’s critique and still benefit from understanding them, as would clinicians. Miriam Solomon’s Making Medical Knowledge (2015) makes important contributions, particularly in the current era of evidence-​based medicine. Further discussions on this topic are beyond the scope of this effort. Important in the present discussion is Kuhn’s notion of incommensurability taken as an epistemic construct. Kuhn held that science was organized around different paradigms, the definition of which is complex and controversial. In the context of medicine, empirics, dogmatists, and rationalists could be ascribed as operating in a different paradigm. Prior to 1920, practitioners in each paradigm often practiced the same. What differed is the rational or explanatory theory. Kuhn’s incommensurability held that believers in one paradigm could not fully appreciate the underlying metaphysics of alternative paradigms. Thus, it is humanly difficult for an empiric practitioner, such as a homeopath, to fully appreciate the position of an allopathic physician and vice versa. Consequently, dealings between the two paradigms take on a polemical tone and produce political and legal confrontations, as in the early 1800s with the founding of the American Institute of Homeopathy and the American Medical Association (AMA). The animosity was captured in the AMA Code of Ethics of 1847 (Chapter 11). However, it would be a mistake to hold that the differences were only political. In today’s medicine, a paradigmatic schism may exist between advocates of evidence-​based medicine, particularly as it is taken as synonymous with randomized controlled trials, and those who believe differently (Cartwright, 2007; Solomon, 2015). Descriptions of evidence-​based medicine as hegemonic play to a polemical and political struggle. Certainly, these are elements. I had a criticism of evidence-​based medicine rejected by the journal Neurology (subsequently published elsewhere [Montgomery and Turkstra,  2003]) because a reviewer stated to the effect that neurologists, at least some members, have struggled too hard and long to make evidence-​based medicine successful. Hypothesis generation may play an important role in the incommensurability of science and the practice of medicine. As hypotheses derive from conceptual interpolations and extrapolations of the informing theory, it should

The Centrality and Origins of Hypotheses

not be surprising that a dominant theory would be associated with dominant hypotheses. As hypotheses are the starting point for the abduction or Fallacy of Confirming the Consequence in the scientific method, the dominant theory dominates the scientific enterprise. Rarely do the different paradigms compete on matters of fact. Rather, they compete on the theoretical explanatory context, which risks the derivative hypotheses becoming quasi-​facts that are used to defeat alternative paradigms. The different although not invalidated hypotheses of alternative paradigms are not seen as plausible, leading to a confirmation bias. In some ways, incommensurability contributes to the Fallacy of Limited Alternatives. The only safeguard against the fallacy is the purposeful consideration of all reasonable alternatives and the avoidance of quasi-​facts. The fact is, science is increasingly competitive, which affects the practice of science. Charles Bazerman analyzed scientific reports in the Philosophical Transactions of the Royal Society from 1665 to 1800 (Bazerman, 1997, pp.  169–​186) describing four stages. During the period of 1665–​1700, scientific papers were uncontested reports of events. From 1700 to 1760, discussions centered over results were added. More theoretical aspects were addressed during the third period, 1760–​1780, where papers “explored the meaning of unusual events through discovery accounts” (Bazerman, 1997, p. 184). From approximately 1790 to 1800, experiments were reported as claims for which the experiments were to constitute evidence. Importantly, reports centered on claims derived solely from the experimenter’s efforts and not on “recognizing the communal project of constructing a world of claims. . . . Although the individual scientist has an interest in convincing readers of a particular set of claims, he does not yet explicitly acknowledge the exact placement of the claims in the larger framework of claims representing the shared knowledge of the discipline” (Bazerman, 1997, p. 184). Arguably, this practice continues to the present day.

Hypothesis Generation in Medical Decisions Various approaches advocated to explain hypothesis generation in the sense of medical decision-​making include pattern recognition and the hypothetico-​ deductive approach. We introduced the latter in Chapter 5 and will discuss it further in Chapter 19. In the pattern recognition approach, one identifies a set of symptoms and signs, which presumably stand apart from the repertoire of human behavior on the basis of their apparent abnormality. The notion of “stands apart” is problematic because of the a priori problem for induction. Yet what constitutes abnormal is a separate issue, particularly in cases involving psychiatric disorders (Foucault, 1965, 1973). Interestingly, the issue of the relative virtues of pure pattern recognition versus abduction was central to the battles between allopathic medicine and the empiric practitioners.

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A number of medical schools adopted pattern recognition explicitly, and more implicitly. The presupposition is that pattern recognition obviates the need for the hypothetico-​deductive approach and hence the need to understand basic underlying mechanisms. This presupposition is often driven by educational professionals who examine clinicians making decisions and then teach students to emulate or mimic those professionals. A teacher need not be knowledgeable in the discipline—​she need only know the ways in which clinicians act. However, such an approach falls prey to the actor–​action distinction, which equates external actions with internal actions; for example, that mimicking the brushstrokes of an accomplished painter does not necessarily lead one to understand that painter’s genius or that acting in a Shakespearian role does not guarantee that the actor understands the play’s meaning and Shakespeare’s understanding of human nature. Any educational approach that fails to appreciate the actor–​action distinction conflates descriptive action for normative action. In other words, it treats that which appears to be done as that which should be done. Attempts have been made to aid physical diagnoses in neurology by putting hypothesis generation on a rational basis (Montgomery et  al., 1985). Symptoms and signs are attributed to a specific functional system, and the anatomical counterparts of the functional system are analyzed. An overlap of anatomies indicates the location of a lesion. The clinician lists tissues present at the site and any possible pathological processes that might arise in those tissues—​thus establishing a pathological differential diagnosis. The pathological differential diagnosis may be ordered into a clinical differential diagnosis based on prior probabilities and ethical considerations. Subsequent evaluation may be used to winnow the differential diagnosis to the cause. For all of its virtues, this approach found little purchase, perhaps related to the ubiquity of pattern recognition (see Chapter 19).

Origin of Hypotheses from Medical Science It will be argued that modern medicine was born in the mid-​nineteenth century with the work of Rudolf Virchow (1821–​1902) and others in cellular pathology (see Chapter  5). The introduction of cellular pathology to medicine was advanced by clinical–​ pathological correlations. The correlations (inductions) of pathology A with disease B created the subsequent abductions that advanced medical diagnosis in the form if a patient who has pathology A will have symptoms c, signs d, and tests e (in Osler’s time, laboratory tests were fairly minimal) is true, and the patient has symptoms c, signs d, and tests e, then she will have pathology A. The abduction belongs to the pathology-​as-​disease paradigm inherent in allopathic medicine.

The Centrality and Origins of Hypotheses

The pathology-​ as-​ disease paradigm introduced the notion of chemotherapy for cancer and other diseases. Paul Ehrlich (1845–​1915) reasoned that specific dyes that targeted certain infectious agents (such as the Gram stain) could be used to destroy those agents. The Gram stain involves the use of crystal violet dye to identify certain types of bacteria. Streptococcus, for example, is gram positive: the organisms retain the crystal violet dye. Neisseria meningitidis (the cause of some forms of meningitis) and other types of bacteria that do not retain the dye are gram negative. Crystal violet dye remains in use as a topical antibacterial. An aniline dye, crystal violet, was investigated for other medical purposes. The same holds true for another dye, prontosil, which similarly demonstrated an antibacterial benefit. It was later determined that prontosil metabolized to sulfanilamide, which constituted the origin of sulfanilamides currently in use today. Work on sulfanilamides led to the development of 6-​mercaptopurine for leukemia, azathioprine for immunosuppression, allopurinol for gout, and other agents for hyperthyroidism, diabetes mellitus, malaria, leprosy, and hypertension (Le Fanu, 2012). These issues are discussed more fully in Chapter 14.

Role of Presuppositions or Metaphors in the Origin of Hypotheses What gave rise to the power of pathology? It did not arise spontaneously out of development of the microscope: Galileo created a compound microscope in 1625, and the first descriptions of microorganisms by Robert Hooke and Anton van Leeuwenhoek appeared in 1676. Yet some 200  years intervened between these events and the work of Rudolf Virchow. Certainly, as early as 1665 Robert Hooke had coined the term “cell,” which would become the basis of cellular pathology. Advancing to pathology, particularly histopathology, required development of the cell theory, which took place in the early nineteenth century. The cell theory developed based on a metaphor that related the cell in biology to the citizen in political theory. This is discussed in detail in Chapter 11. Anatomy, of which cellular pathology is a subset, was not deemed a viable means of understanding health and disease since the ancient Greeks. That there was no demonstrable difference in the anatomy of an organism before and after death, at least prior to putrefaction, argued against anatomy as a source of knowledge of life. The theory of vitalism arose as a result. (Vitalism is discussed in Chapter  14.) The concept of a cell nonetheless became a metaphor that lent to anatomy a physiology and created the anatomy-​as-​physiology paradigm in allopathic medicine. Pathology became synonymous with pathophysiology, an equivalence that still dominates medicine, neurology especially.

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Diagnostic and Statistical Manual V (DSM-​5): Modern-​Day Battle of the Allopaths and Empirics The fifth edition of the Diagnostic and Statistical Manual (DSM-​5), published by the American Psychiatric Association (2013), is intended as a rubric of possible psychiatric diagnoses for purposes of coding, documentation, and billing. The DSM-​5 has a list of diagnoses and their criteria. Its reception illustrates the two different presuppositions as to the nature of knowledge as it relates to psychological and psychiatric disorders. Historically, classifications rested on the similarities and differences in clinical manifestations. The presumption that a similarity of manifestations bespeaks a similarity of underlying mechanisms may be reasonable but is not a prerequisite. Again, the issue is how to deal with the variety of manifestations. This is particularly difficult with psychiatric and psychological problems, where the manifestations exist as a continuum. Nearly every human experiences some depression or sadness of varying duration. Identifying the line that divides a normal state from a diseased state is highly problematic. Obsessive–​compulsive disorder is a useful example. Many individuals have a degree of obsessiveness or compulsivity to which they attribute their success in life. For other individuals, the degree of obsessive compulsiveness reduces their quality of life. The question becomes how to define abnormality. Clearly it is not the manifestations in a qualitative sense but rather in a quantitative sense; for example, the obsessions and compulsions are severe enough to cause distress. Yet if the underlying mechanisms are tied to the manifestations, how does one distinguish between normal and pathological mechanisms? To an allopathic physician, such a strictly phenomenology-​based system represents uncertainty. What is a disease one day is not a disease the next day. An allopath responds by attempting to define the underlying physiochemical processes that clearly differentiate the various modes of human behavior and have predictive value as to the subsequent health of the patient. The advent of neurometabolic imaging, such as positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), has stoked the allopath’s ambitions. Yet the empirics could embrace neurometabolic imaging as diagnostic tools just as any other symptom or sign without the necessity of imputing any more fundamental underlying mechanisms. The imaging could be just epiphenomenal or coincidental. As long as the imaging had sufficient positive and negative predictive value for response to treatment, allusions to underlying mechanisms seem superfluous at best, self-​indulgent at the worst. Clearly, allopathic physicians would not accept such a characterization. They would insist that the changes in neurometabolic imaging clearly relate to the underlying physiology because changes in neurometabolic imaging reflect changes in neuronal activity, and changes in neuronal activities are all

The Centrality and Origins of Hypotheses

that is available for understanding humans behaving. An example of allopathic ambitions, and hence dissatisfaction with the DSM-​5, is seen in the response by Thomas Insel, director of the National Institute for Mental Health, who wrote The goal of this new manual, as with all previous editions, is to provide a common language for describing psychopathology. While DSM has been described as a “Bible” for the field, it is, at best, a dictionary, creating a set of labels and defining each. The strength of each of the editions of DSM has been “reliability”:  Each edition has ensured that clinicians use the same terms in the same ways. The weakness is its lack of validity. Unlike our definitions of ischemic heart disease, lymphoma, or AIDS, the DSM diagnoses are based on a consensus about clusters of clinical symptoms, not any objective laboratory measure. In the rest of medicine, this would be equivalent to creating diagnostic systems based on the nature of chest pain or the quality of fever. Indeed, symptom-​based diagnosis, once common in other areas of medicine, has been largely replaced in the past half century as we have understood that symptoms alone rarely indicate the best choice of treatment.  .  .  . Patients with mental disorders deserve better (http://​ www.nimh.nih.gov/​about/​director/​2013/​transforming-​diagnosis.shtml). However, disease or not disease must be diagnosed, and diagnoses must have some currency among patients, physicians, and healthcare professionals. At the very least, healthcare accounts must have some set of rules based on diagnosis so that they can authorize treatment. The outcome for the DSM-​ 5 was a compromise seen in the joint communication of Insel and Jeffrey A. Lieberman, MD, president-​elect, APA: Patients, families, and insurers can be confident that effective treatments are available and that the DSM is the key resource for delivering the best available care. The National Institute of Mental Health (NIMH) has not changed its position on DSM-​5. As NIMH’s Research Domain Criteria (RDoC) project website states, “The diagnostic categories represented in the DSM-​IV and the International Classification of Diseases-​10 (ICD-​ 10, containing virtually identical disorder codes) remain the contemporary consensus standard for how mental disorders are diagnosed and treated. . . . Yet, what may be realistically feasible today for practitioners is no longer sufficient for researchers (http://​www.nimh.nih.gov/​news/​science-​ news/​ 2 013/​ d sm- ​ 5 - ​ a nd- ​ rdoc- ​ s hared- ​ i nterests.shtml?utm_ ​ s ource=rss_​ readers&utm_​medium=rss&utm_​campaign=rss_​summary).

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Necessary Presuppositions THE METAPHYSICS

The role of the globus pallidus interna (part of the basal ganglia system within the brain) is to select for intended movements and to prevent unintended movements. STUDENT A: I thought that was the function of the brain. PROFESSOR A: Same thing. PROFESSOR B: The role of the frontal lobes is to prevent antisocial behavior. STUDENT B: What shuts down the frontal lobes when one wants to be antisocial? Where is antisocial behavior located in the brain such that it can suppress the frontal lobes to allow antisocial behavior? PROFESSOR B: Well, we are working on that. PROFESSOR A:

The Centrality of Metaphysics (Properly Defined)

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The dependence of hypothesis generation on an overarching theory was described briefly in Chapter 10. Any theory is a mixture of observations within a specific range and gaps between observations. Theory construction involved filling in the gaps, hopefully using the tools of interpolation. Furthermore, the domain covered by theories is extended by extrapolation. The key then is to understand the interplay between theories that generate hypotheses and hypotheses that evolve into theories, particularly in the context of medical decision-​making and medical science. As theories and hypotheses, by definition, extend beyond empirical data and are not considered laws or principles, theories and hypotheses are necessarily metaphysical statements. Hence, medical knowledge necessarily contains metaphysical statements and perspectives. Many scientists and clinicians might object to any suggestion that they dabble in metaphysical speculation. Indeed, the formation of modern science

Necessary Presuppositions

was in reaction to and rejection of the metaphysical methods of the medieval scholastic natural philosophers. Whether such criticism is completely warranted is another matter. Unfortunately, the metaphysics of the medieval scholastic natural philosophers often invoked God, perhaps for political reasons (Thomas Hobbes [1588–​1679] did not and suffered accusations of being an atheist and was ostracized, although fortunately not prosecuted as others were; see Mintz, 1962) or as a deus ex machina, a way to escape once one was painted into an intellectual corner. Scientists such as Galileo and Descartes dismissed the metaphysics of the medieval scholastic natural philosophers who were said to be concerned about “how many angels can dance on the head of a pin.” Yet, such a concern was never voiced by respected scholastics but attributed to them by opponents as mockery (Makari, 2015). Metaphysics, as used here, follows from Aristotle’s notion that metaphysics follows from physics (an empirical endeavor) as a way of explicating phenomena. Such explication would follow from what Aristotle called first principles, which are the underlying principles and laws that, in combination, account for the phenomena. In this sense, Aristotle’s metaphysics is similar to the position of the rationalist/​allopathic physicians today: that variety is variations on canonical forms (principles and laws) that can be combined to explain any single observation, such as a patient’s unique manifestations. In this sense, it is legitimate and indeed necessary to consider the metaphysics of medical knowledge and decision-​making. Theories are a product of understanding, which is different from knowl­ edge. “Understanding” as used here is from a Kantian perspective as described in Chapter 1. In addition to containing knowledge—​facts of the matter—​and theories—​complex amalgams of hypotheses and facts—​understanding also has agency. This agency is taken from Kant’s notion of the transcendental aesthetic in his Critique of Pure Reason (1781), particularly the synthetic a priori. The synthetic a priori is a means for apprehension of the perceptions afforded by the sensibilities to result in experience that then forms the basis for knowledge—​one perceives what one is prepared to perceive. Aristotle’s Physics rightly can be considered a work of natural science, but one unlike his other more biological treaties. In a sense, Physics is an exercise in the abstractions from natural phenomena. These abstractions are the changes in natural things themselves, such as generation, decay, and motion. The convergence and divergence of material into things where different things contain the same material and the same things contain different materials are a challenge in any attempt to organize phenomena. For example, one recognizes a house to be a thing, but a house can be made of brick, stone, or wood. The same brick, stone, or wood can be used to construct different things. How then to relate brick, stone, and wood to houses and bridges? In many ways, Physics is an effort to organize phenomenological variability (see Chapter 6).

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It may be more practical to begin an analysis of medical science as this often is the source of theories that inform medical decision-​making. Critical analysis of those theories illuminates the prior metaphysics, which contain the presuppositions necessary for current medical science and practice.

Allopathic Medicine and Science Allopathic physicians clearly align themselves with science, as is clear from the American Medical Association (AMA) Code of Ethics of 1847 and its subsequent reforms following the Flexner Report of 1910 (see Chapter  5). Given that there are at least two types of science—​the strictly empirical Baconian and Cartesian reasoning from empirically derived principles—​ the question is what form of science was adopted by allopathic physicians? Given the close association of schools of medicine and research institutions consequent to the Flexnerian revolution, Baconian empirical science could be said to hold sway, although if it has been ultimately adopted by allopathic physicians, as distinct from allopathic academic/​scientific physicians, remains a question. With the adoption of Baconian science, there was a strong reaction against philosophy and, by extension, Cartesian science. As noted, perhaps the most vociferous critics of the AMA Code of Ethics were the scientists/​physicians who argued that the advances in science would render ethics obsolete and irrelevant (Warner, 1991). Abraham Jacobi wrote in 1886, “no scientific body places prohibitory rules upon scientific men in matters of ethics . . . the absence of ethical codes [is testimony to] its scientific spirit” (quoted in Warner, 1991, p. 466). Newton and Galileo all eschewed the natural philosophers, whom they saw as schoolmen (arguing from texts rather than observation and experimentation) vying in metaphysics. Newton said “Hypotheses non fingo”—​ “I feign no hypotheses” (in other words, just the facts and nothing but the facts). Many scholastics took the writing of the ancient Greek philosophers, particularly Aristotle, as their principal source of theory. This created the impression that they eschewed empiric studies. However, this is not true. First, Aristotle was an empiricist (Leroi, 2014), and thus, by extension, the scholastics could be described as inheriting Aristotle’s empiricism. Also, historical analysis demonstrates that many medieval natural philosophers engaged in scientific enquiry that resonates well with modern science (Freely, 2012; Hannam, 2011). Newton’s statement seems as odds with the fact that he provided rules for the interpretation of the Bible in his General Scholium, in the second edition of his Philosophiæ Naturalis Principia Mathematica, in 1713. Galileo rejected

Necessary Presuppositions

Kepler’s elliptical orbits for the planets because the circle was the “perfect” figure rather than the ellipse (Topper, 2010). One could easily argue that such a position smacks more of metaphysics of the kind precisely rejected by Newton and Galileo. Whether or not Baconian science actually needs metaphysics is beyond the scope of this Chapter, although the Fallacy of Induction and the a priori problem of induction argue that there is no such thing as strict empiricism. Rather, it is difficult to escape metaphysics. Thus, metaphysics plays an important role in medical science and medical reasoning—​whether or not it is recognized. It is important to clearly differentiate claims and reasoning due to metaphysics and hence, by definition, not founded exclusively in observation. Certainly, implicit metaphysics should not have the same evidentiary status of established observations, but, unfortunately, they do. In these cases, they become quasi-​fact.

Role of Theory in Shaping Observation The cases introducing this Chapter typify some of the problematic nature of quasi-​ facts derived from the implicit metaphysics held by the scientist and physician. In the case of the globus pallidus interna, disorders of the basal ganglia (of which the globus pallidus interna is part) have phenomena dichotomized into hypokinetic movements (slow movements) and akinesia (loss of certain mostly habitual movements), of which Parkinson’s disease is an example, and hyperkinetic involuntary movements, of which Huntington’s disease is an example. Hypokinesia results if the globus pallidus interna as a functional gate is “too closed,” and hyperkinesia results if the gate is “too open.” Through a series of abductions, hypokinesia has become associated with excessive neuronal activity in the globus pallidus interna, whereas hyperkinesia is associated with underactivity. The dynamics are one-​dimensional push–​pull systems. In other words, the dynamics of the phenomena is only along one dimension; that is, the relative amounts of movement and the dynamics of the causal mechanisms are one-​dimensional. With the metaphor established, the metaphor evolved to describe a role of the globus pallidus interna as selecting movement by allowing some and prohibiting other movements. Whether the reduction of the dynamics into one-​dimensional push–​pull systems started with clinical phenomena or neuronal pathophysiology is unknown. What evidence supports the dichotomization and the reduction of the observations to a one-​ dimensional push–​ pull system? If in every case of hypokinesia one never simultaneously sees hyperkinesia, and, in case of hyperkinesia, one never simultaneously sees hypokinesia, one might argue

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that they are reciprocally related, according to Mill’s joint method of agreement and difference (Mill, 1843, p.  463). However, patients with Parkinson’s disease have hypokinesia, thought to be a direct effect of their disease, and simultaneously have hyperkinesia secondary to medications. Similarly, patients with Huntington’s disease have both hyperkinesia and hypokinesia simultaneously. These observations would seem to invalidate the conception of one-​ dimensional push–​pull systems. The conceptual architecture of the relationship of the frontal lobes again is a one-​dimensional push–​pull system. The frontal lobes are posited as oppositional to the tendency to be antisocial, presumably embodied in another structure in which the frontal lobes may be reciprocally connected. To my knowledge, exactly what part of the brain is the seat of antisocial behavior such that its release from inhibition results in actualized antisocial behavior has never been specified.

Aristotle’s Notion of the Contraries The central epistemic conundrum that drives the metaphysical approach to health and disease is how to explain variety. Assuming that no economical set of explanatory principles were possible, the empirics are confronted with the Solipsism of the Present Moment (no inferences from the past are possible). Assuming an underlying economical set of principles, the rationalist/​allopathic clinicians and scientists were confronted with the inverse problem. Furthermore, the rationalist/​allopathic clinicians and scientists are tempted to use powerful methods inappropriately, such as Aristotle’s notion of the contraries, in order to economize the set of explanatory principles. The rise of the cell theory, its derivative the neuronal doctrine, and histopathology created a model of dynamics that are one-​dimensional push–​pull systems. Indeed, it can be argued that dynamics that are one-​dimensional push–​pull systems are a necessary presupposition for the advancement of medical knowledge, at least in the manner of allopathic clinicians and scientists, at least initially. The same presupposition continues to permeate modern neurology and psychiatry (Arikha, 2008). The variability often is so great as to constitute a continuum without separate sharp boundaries to consider distinct groups. Since the ancient Greeks, one response to such a continuum of variability has been to take the position that only the extremes have ontological status—​that is, they are entities—​and that all manifestations are some admixture; otherwise, there are as many ontological entities as there are degrees in the continuum (discussed in greater detail later). A further economization can be achieved by holding that the extremes of the continuum are related by opposition, as discussed in greater detail in Chapter 12.

Necessary Presuppositions

The Triumph of Allopathic Medicine (the Modern Doctor of Medicine and Doctor of Osteopathic Medicine) and Its Presuppositions It would be fair to say that allopathic medicine dominates current medical practices. Even osteopathic medicine has largely become subsumed under allopathic medicine. Again, the dominant theme, the presupposition that underwrote early notions of health and disease and continues to underlie much of current neurology and psychiatry, is mechanisms of one-​dimensional push–​ pull systems with the extremes or poles of the dimension being in opposition. As demonstrated later, such a mechanistic (and nearly mechanical) metaphysical presupposition is a natural result when confronting the myriad and variety of the manifestations of health, disease, and behavior. But first, the emerging dominance of allopathic medicine. Chiropractic alternatives continue but largely have narrowed the range of diseases treated, concentrating on musculoskeletal problems and more often claiming legitimacy through legislative and legal means rather than on science, for example, in Wilk v. American Medical Association, 895 F.2d 352 (7th Cir. 1990), discussed in Chapter 4. The dominance of allopathic medicine was firmly established by the Flexner Report for the Carnegie Foundation (Flexner, 1910; see Chapter 6). The Flexner Report sealed the dominance of the allopathic physicians, but, paradoxically, it took decades for scientific medicine to produce results justifying the faith in science held by the allopathic physicians in 1847. Indeed, the faith in science seen in the AMA Code of Ethics of 1847 is a striking example of scientism, which is a belief that science will provide the answers even in the absence of any evidence that it has done so and thus will do so.

Rationalism (Reductionism), Empiricism, and Dogmatism and Their Presuppositions The allopathic metaphysical presupposition was and is that human behavior—​ health and disease—​is based on some economical set of fundamental principles. As will be seen, reductionism is a powerful means of economizing the set of explanatory principles, even though it is heir to the Fallacy of Four Terms. This Galenic approach characteristic of allopathic medicine persists to this day in many areas of medicine (Arikha, 2008). Yet the notion that symptoms and signs represent a relative excess or deficiency of some entity or state, such as a humor, presents problems for the allopathic rationalist physician. The symptoms and signs, in themselves, do not immediately implicate mechanisms of relative excesses or deficiencies, as discussed in Chapter 12. The challenge for the allopathic physician was to understand how pathology or injury associated with a specific part of the body would alter normal functions to generate

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a relative excess or deficiency. For example, Benjamin Rush (1746–​1813) held that disease was a consequence of abnormal extension or contraction of blood vessels, perhaps influenced greatly by the research of William Harvey (1578–​ 1657) on circulation. Thus, abnormal extension or contraction of blood vessels in a specific body part would result in excessive or loss of function associated with the body part. As discussed later, the rise of the germ theory and histopathology following the cell theory facilitated greatly a link from pathoetiology to pathological cells, tissues, and organs to symptoms and signs. Indeed, it was the remarkable ability of Sir William Osler to relate pathology to symptoms and signs that rapidly advanced medicine at the beginning of the twentieth century, as well as advancing Osler’s fame and fortune. However, the success of equating histopathology to pathoetiology to pathophysiology to clinical syndromes (collections of symptoms and signs) had its cost, as discussed later. For empirics, the transition from symptoms and signs to treatment was straightforward and unencumbered by any need for causal explanation based on a reductionist account. For empirics, the question is, then, what to make of the symptoms and signs and how to relate them to a specific treatment. For empirics, the symptoms and signs reflected the body’s attempt to fight or counter the disease or disorder. Certainly, until the advent of the germ theory and histopathology following the cell theory and subsequent clinical–​ pathological correlations, this presupposition by the empirics was as least as good or bad as allopathic medicine. Neither enjoyed any advantage in the treatment of patients. For empirics, the method then was to find treatments that produced similar symptoms and signs as those of the disease and administer those as treatments. However, this presented a difficulty for the empirics, as the precise manifestations of symptoms and signs probably are as numerous as there are patients. Thus, the treatment given to one person would be unsuitable for another person if there were any differences in the symptoms and signs. A treatment had to be concocted that matched the patient uniquely. This also meant that it would be very difficult to generalize from one patient to another as each patient would be a unique phenomenon (a variation on the Solipsism of the Present Moment). The treatment of any single patient would become an entire study unto itself. Furthermore, what specific symptoms and signs should be considered—​the a priori problem of induction. Thus, there was no method of simplification or economizing. The other competitors to allopathic medicine were the dogmatics, for whom the practice of medicine was prescribed by certain predecessors. There was no need for research to uncover explanatory fundamentals. Dogmatics were also called methodists, as medicine was practiced by a fixed method. One might ultimately call this form of medicine a “cookbook” approach. Lest this approach be dismissed immediately, dogmatics would be right at home

Necessary Presuppositions

following guidelines for treatment published by numerous medical professional organizations. This is particularly true with guidelines purported to be based on evidence-​based medicine synonymous with randomized controlled trials. The research design and hypotheses tested by randomized controlled trials do not require any explanatory power, just statistical significance. If administration of a pulverized kitchen sink was shown to result in statistically significant improvements in condition A better than any alternative and with fewer adverse effects (the latter in its widest connotation), modern clinicians would have little choice but to prescribe a pulverized kitchen sink, much as a dogmatic practitioner would.

Dealing with Variety and Its Consequent Uncertainty Variability plagued the allopathic physicians. Lest they accept that no knowl­ edge transcends the individual patient, allopathic physicians were forced to argue that there must be some economic set of principles that makes diagnosis and treatment more tractable. The approach taken by the Galen follows from Aristotle’s notion (actually going back at least to Anaximander [610–​546 bce]) that the domain of dynamics of nature could at least be divided in half by pairing seemingly opposition dynamics into a single entity where the opposing dynamics are just the extremes or the poles of a single dimension. Every entity is some combination of contraries. “The physicists . . . have two modes of explanation,” Aristotle wrote. “The first set make the underlying body one—​either one of the three or something else which is denser than fire and rarer than air—​ then generate everything else from this, and obtain in multiplicity by condensation and rarefaction. Now these are contraries which may be generalized into ‘excess and defect’ ” (Aristotle, 2001). The second set, according to Aristotle, consists of “contrarieties [that] are contained in the one and emerge from it by segregation” and also produces “other things from their mixture by segregation” (Aristotle, 2001). Aristotle dismissed the second set. The Aristotelian approach thus dichotomizes ontology into subsets of entities that produce the phenomena observed by mixing. Furthermore, the dichotomization necessarily details a form of dynamics, meaning how the extremes relate to each other; for example, as opposites, such as black and white. This opposition defines the dynamics and is best described as one-​ dimensional push–​pull systems. For Aristotle’s ontology, there was air, earth, fire, and water. Galen transformed these physical or object-​like ontologies into a medical ontology of the four humors consisting of phlegmatic, choleric, melancholic, and sanguine, where the balance among them determined the observation or behavior. The presuppositions of allopathic medicine now evolved to one-​dimensional push–​pull reciprocal interactions between entities or forces that are the antithesis of each other.

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The theme of reducing variability to some admixture or set of limited fundamentals with opposing dynamics continues throughout the history of medicine. For Benjamin Rush (1746–​1813), it was extension versus constriction of blood vessels—​probably following the discovery of circulation by William Harvey (1578–​1657). But is this much different, conceptually, from a relative excess or deficit of neurotransmitters that underlie the conceptualization of depression (insufficient dopamine or norepinephrine), schizophrenia (excessive dopamine), and movement disorders such as Parkinson’s disease (deficiency of dopamine)?

Allopathic Medicine and Reductionism From a technical perspective, reductionism is robust if what is meant is the increasing technological capacity to use ever sharper “knives” or bigger “hammers” to dissect or break any relevant substance into increasingly smaller “pieces”; here “knives,” “hammers,” and “pieces” are thought in their widest connotation. The principal issues are whether there is some limit on how finely things can be “chopped” and the subsequent pieces can be “seen.” This form of reductionism is called methodological reductionism. But that is not the interesting question of medical (as well as ordinary) science. If the final pieces resulting from methodological reductionism result in an entity very different from the starting material, then there is change in the substance, and this form of reductionism is referred to as ontological reductionism. The real value of reduction is whether the “pieces” can be recombined or reconstructed to the original phenomena and, in doing so, create new knowl­ edge or new utilities such as new treatments. The real value of reductionism is not in reduction or deconstruction, but in reconstruction. The scientific scheme of reduction would be better described as scientific constructionism. The reconstruction may result in a new explanation, even if there is no change in the methods or change in the nature of the substances. In this case, what is changed is the explanation of the original material that follows from the reconstruction. This form of reductionism is called theoretical reductionism. Certainly, the presupposition or faith is that reconstruction follows from reduction in a relatively straightforward manner such that it is rarely given any thought. Great mischief is created when one form of reductionism is confused or conflated with another. One could argue that only ontological or theoretical reductionism gives rise to new knowledge but that technology focuses on methodological reductionism. The danger is when the new products of methodological reductionism are given ontological and theoretical status. Disease becomes abnormal tests. Conflation likely relates to the implicit presumption that reconstruction of the original from the reduced parts is unproblematic and, consequently, naturally leads to an ontology that is certain. But this is an article of

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faith. Given the concerns regarding irreproducibility in biomedical research, the faith may be misplaced (Chapter 17). The numbers of medical treatments created by rational reconstruction of reductionist medical science are very few indeed. For example, antibiotics, antihypertensives, antidiabetic oral agents, and antidepressants were the result of serendipity, at least initially. Even after several billions of US dollars have been spent on the Human Genome Project, only two gene therapy treatments have been approved by the US Food and Drug Administration. Clearly, new treatments based on a complete application of reductionism to the human genome and subsequent reconstruction have failed to materialize, at least as yet. Attributing the disappointments of basic science to “growing pains” may be simplistic, as is the notion that spending more effort and money eventually will give returns on the investments. A  serious question is whether reductionism methods at the same time make reconstructionism problematic. Methodological reductionism risks irreversible loss of information, according to the Second Law of Thermodynamics as Applied to Information. Methodological reductionism risks loss of the dynamics; for example, reduction to static entities. These physical entities are “cleaved” easily by modern scientific technology. In fact, this very fundamental question about physical entities (e.g., the anatomy) and physiology (e.g., the dynamics) has been discussed since the ancient Greeks. The recognition of the loss of dynamics with reductionism was the basis for the controversies between dissection (in the sense of necropsy of the dead) and vivisection (dissection of the living) (Zaner, 1998). In fact, the only way forward through this controversy for modern medicine came with the cell theory circa 1850 that extrapolated from anatomy to physiology, as will be discussed later in this chapter. F. M. R. Walshe, a prominent British neurologist, pointed out the difficulty of such extrapolations. For example, patients with damage to the cerebellum develop shaking or tremor in their movements. Some have suggested the normal function of the cerebellum is to suppress shaking or tremor. Similarly, if a part of an automobile becomes damaged and makes a “clunking” sound, would one hold that the purpose of the automobile part is to prevent “clunking” sounds? Biology is more than describing parts and includes interactions, hence dynamics. In many cases, medical science ignored or, at best, minimized dynamics. Dynamics often are difficult to analyze and, for expediency, are inferred from relatively stable states at equilibrium. Consider the situation of a reversible chemical reaction where chemical A combines with chemical B to produce chemical AB at a rate given by k1. The rate at which chemical AB was generated is determined by the concentrations of A and B. Typically, an “opposing” reaction causes chemical AB to dissociate into chemicals A and B and by a rate given by k2 and the concentration of chemical AB. At equilibrium, the rate that chemicals A and B produce chemical AB equals the rate that chemical AB produces chemicals A and B. The final concentrations of chemicals A, B, and

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AB are used to determine the rates of the two opposing reactions, given by k1 and k2, to infer the dynamics. However, the dynamics are not static even if the concentrations of chemicals A, B, and AB remain constant. Molecules or atoms A and B are forming atom AB constantly, and atom AB is forming atoms A and B constantly. Complex and chaotic biological systems that are living do not operate at equilibrium and thus the simplifying methodological reductionism to state-​ steady concentrations of reactants can be misleading. These systems typically operate far from thermodynamic equilibrium and demonstrate chaos and complexity. These systems demonstrate dependence on initial conditions and unpredictability. For example, a very small change in the initial state of the system produces disproportionately large and unpredictable final states. While a remarkable amount is known about the chemistry and physics of water—​the combination of hydrogen and oxygen in a 2:1 ratio—​it is impossible to predict the pattern of a snowflake. Despite all that is known about the physics of air and turbulence, it is impossible to predict the weather except on extremely short time frames. Rather, weather predictions borrow more from the just-​recent weather than they do based on predications from the elements achieved by reductionism. Even in mathematics, where nearly every mathematical statement can be reduced to a specific and economical set of axioms, there are mathematical statements that defy prediction. These phenomena are examples of the emerging discipline of chaos and complex systems (further discussed in Chapter 14). Other examples of the fundamental limits of reconstruction include Gödel’s Incompleteness Theorems. These observations would suggest that the reductionist agenda may be doomed to never be fully successful in the explanation of nature, let  alone human health and disease, at least at levels of complexity or dynamics that are interesting.

Cell Theory, the Rise of Pathology (Particularly Histopathology), and Pathological–​Clinical Correlations of William Osler A boost in the scientific status of allopathic clinicians and scientists came with the claim of the cell as a fundamental unit of life and formulation of the cell theory in 1838 by Matthias Jakob Schleiden (1804–​1881), Theodor Schwann (1810–​ 1882), and Rudolf Virchow (1821–​ 1902). However, Robert Hooke (1635–​1703) described the cell in 1665 and Antonie Philips van Leeuwenhoek (1632–​1723) described cells much earlier. Why did it take nearly 200 years for the cell theory to be formulated? Certainly, the cell theory did not have to await the discovery of cells. It may be that the cell theory was not a new anatomical theory. Rather, the cell theory was a theory of function, physiology, and dynamics. The function of the organism, such as a human, was to be understood by reconstruction of

Necessary Presuppositions

the functions of the organ system, which in turn were reconstructed from the functions of tissues, and, ultimately, from the function of cells. However, the actual reasoning to generate the cell theory was the reverse. The function of the organ was extrapolated to the tissue, which in turn was extrapolated to the cell. As with most theories, formulation requires a strong metaphor. This was to be provided by social–​political theory. As political theory moved from the divine right of kings to kings deriving their authority from the citizens, the dynamics of the citizens became a key concern. Interestingly, key political theories were offered by Francis Bacon (1561–​1626) and, most importantly, by Thomas Hobbes (1588–​1679) in his Leviathan or the Matter, Forme and Power of a Commonwealth, Ecclesiasticall and Civill (1651), often referred to as just the Leviathan. The functions of the state (counterpart—​organs) were conceptualized as functions of component villages (counterpart—​tissues), which in turn were explicated by the functions of individual citizens (counterpart—​cells). Thus, the dynamics of the citizen constituted the fundamental dynamics, which were recombined to eventually explain the dynamics of the state (Canguilhem, 2012, pp.  68–​71; Reynolds, 2007). Importantly, note the tautological or circular reasoning. Ernst Haeckel (1834–​1919) noted a distributed arrangement in plants and a hierarchical arrangement in animals where the body is subordinate, as a whole, to the nervous system (Reynolds, 2007). This hierarchical arrangement parallels remarkably with the description of the relationship between the sovereign and the subjects in Hobbes’s Leviathan, in which the sovereign manifests the will of the citizenry perfectly, and, consequently, no discord is possible. This notion is clearly represented in the frontispiece of Leviathan (Figure 11.1), where the body of the sovereign (the Leviathan) is composed of the bodies of the citizenry. The political theoretical origin of the cell theory held that the cells were the functional unit from which the behavior of the whole could be reconstructed, another presupposition sympathetic to allopathic medicine. But then how to describe that function? It followed that the functions of the human were to follow from the functions of the organs. Thus, whatever functions can be extracted from humans at a phenomenological level, those functions would be expected in the organs. As the functions of the organs were to follow the functions of the tissue, the functions of the organs would be found in the tissue. Finally, if the functions of the tissue are derived from the cells, then the functions of the tissues are to be found in the functions of the cells. By the Principle of Transitivity, the functions of humans were to be found in the functions of the cells. Rudolf Virchow (1821–​1902) wrote “unity of life in all living things finds its concrete representation in the cell. . . . It is an organism in miniature; in itself it is already capable on a separate existence . . . for the cell is either the living individual itself or it contains—​in outline at least—​what we accustom to designate as such” (Virchow, 1958, p. 105).

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FIGURE 11.1 

Frontispiece to the initial publication of The Leviathan by Thomas Hobbes (1652). The sovereign (the Leviathan) is depicted as holding the symbols of secular (the sword) and

Necessary Presuppositions

ecclesiastical (the bishop’s staff) power. Of note, the body of the sovereign is composed of the bodies of the citizenry.

The fundamental representation of living attributed to the cell will be shown remarkably similar to that of Aristotle’s notion of potentiality and actualization. Consequently, the cell theory will inherit the conundrums Aristotle faced, addressed in detail in Chapter 12. For example, the function of the arm is to move; thus the function of the muscle is to move by contraction. This reasoning then extends that the function of the cells that make up the muscle is also to contract. This may seem unproblematic. But consider the function of the human to not engage in antisocial behavior; the extrapolation is that it is the function of brain cells to prevent the engagement in antisocial behavior, which is exactly the position of Professor B in the case at the beginning of the chapter. It is not clear how a single neuron integrates (adds) electrical pulses it receives and transforms the result into another set of pulses that is sent off to other cells and contains all the complex information necessary to prevent antisocial behavior. The process of carrying forward the functions of whole humans to organs to tissues to cells is another presupposition of the allopathic physician. However, this represents the Mereological Fallacy, which is the attribution to the part of the function of the whole. In this case, it is the Fallacy of Division. The Fallacy of Composition attributes the function of the part to the whole.

Mereological Fallacy The fundamental presupposition of the cell theory is that cells are fundamental functional units, but, at the time, the cell was an anatomical notion. The question becomes how to ascribe functions to cells. This conundrum was noted by Aristotle, who said: Yet, to say that it is the soul which is angry is as inexact as it would to say that it is the soul that weaves webs or builds houses. It is doubtless better to avoid saying that the soul [or a specific part of the brain as in consciousness] pities or learns or thinks, and rather to say that is the man [the entire nervous system] who does this with his soul. What we mean is not that the movement is in the soul, but that sometimes it terminates in the soul and sometimes starts from it, sensation e.g. coming from without inwards, and reminiscence starting for the soul and terminating with the movements, actual or residual, in the sense organs. (De Anima 408b 12–​20, Aristotle, 2001)

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The Conundrum Is the Result of the Inverse Problem Observing a blind person, one sees the hand move in a manner qualitatively the same as if the eyes were intact, although the target may be missed. From such similar observations of other behaviors, fragments of behavior become associated with fragments of the organism in John Stuart Mill’s Method of Difference. This is not unproblematic, as noted by F. M. R. Walshe (described previously). The frontal lobes are thought to suppress antisocial behavior because antisocial behavior manifests when the frontal lobes are damaged. But what if the cells, in this case in the frontal lobes, are not the fundamental units of function? Rather, it is some cooperative among a set of neurons, perhaps going beyond just the frontal lobes, that ultimately leads to behaviors that are not antisocial. When that cooperative action is lost, there is a subsequent self-​reorganization whose output can only lead to antisocial behavior. Consequently, there is no need to invoke some specific center in the brain for the generation of antisocial behavior and thus, by extension, some center to generate social behavior. Consider two approaches to 10 persons building an automobile. One utilizes a conveyor belt—​each of the 10 persons conducts a unique action that contributes to building the automobile. Alternatively, all 10 persons may be engaged simultaneously in each action. In the case of an assembly line, one could remove 1 person and the consequence will be a dysfunctional automobile. Similarly, in the case where all 10 persons build the automobile simultaneously and 1 fails, the result will be a dysfunctional automobile. When presented with a dysfunctional automobile consequent to the failure of 1 person, it is problematic to know whether the automobile was built on an assembly line or by simultaneous cooperation by the 9 persons still functioning. This is an example of the inverse problem. It is important to note that an experiment or observation that stops when a finding is first presented, such as disabling worker A and finding a dysfunctional automobile, there is an inclination to stop and attribute the dysfunction to worker A and ignore the other workers. This was noted by Tolstoy, who wrote The totality of causes of phenomena is inaccessible to the human mind. But the need to seek causes has been put into the soul of man. And the human mind, without grasping in their countlessness and complexity the conditions of phenomena, of which each separately may appear as a cause, takes hold of the first, most comprehensible approximation and says: here is the cause. (Tolstoy, 1869) Yet, this is an example of the Fallacy of Limited Alternatives. Close on the heels of the cell theory came cellular pathology or histopathology. Indeed, Rudolf Virchow (1821–​1902), prominent in origination of the cell theory, was an early pioneer in histopathology. The functions of

Necessary Presuppositions

the cell theory could be extrapolated readily to expectations of the clinical consequences of the histopathologies. This program was expanded rapidly and given creditability by Sir William Osler (1849–​1919) through his ability to link clinical syndromes to underlying pathologies (Bliss, 1999). Osler was perhaps one of the most respected physicians in history, assuming leadership positions at the University of Pennsylvania, Johns Hopkins, and Oxford. What is particularly remarkable are the numerous eponyms given to various signs that patients present with and that are used for diagnosis, such as Osler’s nodes, which are nodules on fingertips or toes associated with subacute bacterial endocarditis (infection of the heart valves). However, the remarkable power of Osler’s eponyms is that their presence predicts the histopathology of disease rather than merely a correlation. With the rise of the cell theory, histopathology became pathophysiology, and Osler’s findings on physical examination then related to pathophysiology. Osler was a remarkable pathologist, personally undertaking autopsies and postmortem examinations on his own patients. Osler, perhaps above all physicians, reinforced allopathic medicine by practical demonstration of the power of reductionism. The clinician, examining the patient, could predict the pathology. At the time, predicting pathology did not necessarily lead to new, better, or more scientific treatments. Osler was still recommending bloodletting in 1923. Osler advanced the science and skill of diagnosis, if not treatment. Since antiquity, diagnosis was the raison d’être for physicians. The power to explain and predict comes from the patient’s immense need to understand, for example, the “why me” question and to predict the “what is going to happen to me” question.

Neurology and the Neuron Doctrine The neuron doctrine, advanced by Ramon y Cajal (1852–​1934), is a special case of the cell theory. As in cell theory, neurons were held to be anatomically independent rather than continuous in a syncytium, such as the myocardium. The view that the nervous system was composed of neurons anatomically in continuity with each other was called the reticularist theory, championed by Camillo Golgi (1843–​1926) (Kruger and Otis, 2007). The neuron also was polarized, with a distinct input section receiving connections through synapses, such as the dendrites and neuronal cell body (soma), and an output section, such as the axons. Interestingly, Cajal used the same staining methods for visualizing neurons as did Golgi, yet the two came to very different conclusions. Cajal first studied embryonic animal nervous systems, which allowed him to see neurons that were physically separate from each other. Thus, evidence supporting Cajal and opposing Golgi came from developing embryos. The generalization of those

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findings to the adult animal required a leap of faith rather than an act of science, given what was known at the time. Also, it has been alleged that Cajal was not above drawing in synapses in his anatomical renderings (Fishman, 2012, pp. 226–​229). Cajal remains revered in the neuroscientific community despite the fact that exceptions have been found to virtually every postulate of the neuron doctrine (Kruger and Otis, 2007). Nonetheless, the neuron doctrine still denominates the ontological theory and, consequently, severely constrains epistemological considerations. The neuron doctrine arose from controversies regarding the localization of functions attributed to the brain. Early locationists included Franz Joseph Gall (1758–​1828), who popularized phrenology, whereas Pierre Flourens (1794–​ 1867) opposed it. The issue apparently was settled with the work of Pierre Paul Broca (1824–​1880) demonstrating the localization of language function. Protecting the neuron doctrine appears to have been at least part of Cajal’s motives. The resonance between the localization of brain functions and the neuron as the fundamental functioning unit likely was mutually reinforcing and resulted in the cardinal cell hypothesis (also referred to the grandmother cell hypothesis—​somewhere in the brain, a specific neuron responds when one sees one’s grandmother and only one’s grandmother). A single neuron represents, encodes, or otherwise instantiates a single specific function. The consequence is taking a strictly static anatomical account and overlying on it a physiology—​ the Mereological Fallacy.

A Case Study The neuron doctrine bred the use of “circuit or wiring diagrams,” which linked various structures in the brain involved in specific functions, discussed in greater detail later. Although most of the following discussion relates to neurology and neuroscience (because I  am a neurologist and neuroscientist), metabolic networks, the domain of systems biology is analogous. Thus, concepts illuminated in the context of the nervous system may find application in metabolic networks within all cells and in the communications between all cells. Permit me to use an example from a field in which I  have some direct knowl­edge and experience: the pathophysiological mechanisms underlying the neurological disorders of movement such as Parkinson’s disease and chorea. In current use, the syndrome parkinsonism has the canonical form of slowness of movement (hypokinesia) in the absence of any paralysis, while Huntington’s disease has a canonical form of involuntary movements (hyperkinesia). Anatomical abnormalities associated with these conditions include degeneration in the substantia nigra pars compacta and striatum, respectively. These

Necessary Presuppositions

structures are part of the basal ganglia, a set of collections of neurons beneath the cerebral cortex (Jackson, 1958, pp. 238–​245). Why was the substantia nigra pars compacta selected as the pathology associated with Parkinson’s disease in view of the extensive and varied pathology now known to exist in Parkinson’s disease (Braak et  al., 2003)? Konstantin Nikolaevich Tretiakoff (1892–​1958) first described degeneration of the substantia nigra pars compacta as causal to Parkinson’s disease. He also noted the invariable association with pathology elsewhere, a point made by his contemporaries, including cases of parkinsonism without degeneration of the substantia nigra pars compacta (Lees et  al., 2008). Subsequent history provided increasing instantiations of parkinsonism with varied lesions outside of the substantia nigra pars compacta, but this disproves that parkinsonism is only associated with lesions of the substantia nigra pars compacta. Nevertheless, parkinsonism is taken as synonymous with the loss of substantia nigra pars compacta function, which has misled the neuroscience of motor control. Subsequent studies demonstrated that dopamine depletion in laboratory animals given reserpine displayed symptoms similar to parkinsonism. The discovery that efferent neurons of the substantia nigra pars compacta utilize dopamine as their neurotransmitter provides a consilience of evidence, or so it would seem. The risk of the Fallacy of Four Terms is applicable here. The bridging term “parkinsonism” in the major premise linked to rodents given reserpine, and the bridging term in the minor premise linking parkinsonism in humans risks the fallacy if the two forms of the bridging term are not synonymous in every potentially relevant way. The potentially problematic nature of the issue of Parkinson’s disease as a disorder of dopamine depletion is still unresolved, even if largely ignored (see the issue of “symptoms without evidence of dopamine depletion” in Chapter 6). Chemical agents, particularly levodopa, were used deliberately to replace the effect of dopamine as it would be utilized by normal substantia nigra pars compacta. This goal has not been met and is unlikely to be met anytime soon (had it been met, Parkinson’s disease would have been “cured” by levodopa therapy and fetal dopamine transplants). Nevertheless, levodopa was a godsend for patients with parkinsonism. Interestingly, levodopa was not the first medication effective for Parkinson’s disease. Anticholinergic medications have been used since the time of Jean-​Martin Charcot (1825–​1893). Observations of the clinical effectiveness of drugs that block acetylcholine neurotransmitter function and drugs that mimic the effect of dopamine led to an almost expected theory (given Aristotle’s notion of contraries) called the cholinergic–​dopaminergic imbalance theory of parkinsonism and hyperkinetic disorders. This theory posited a relative excess of acetylcholine and a relative deficiency of dopamine as causal to Parkinson’s disease. The theory also predicts a relative excess of dopamine as causing the

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converse of hypokinesia, that being hyperkinesia. Indeed, agents that block or deplete dopamine reduce involuntary movements, and anticholinergic agents are thought to worsen hyperkinesia. Galen would not have been prouder. In the early 1980s, the cholinergic–​dopaminergic imbalance theory ceased any mention in the literature, almost as though the Ministry of Truth from George Orwell’s novel 1984 (published in 1949) was at work revising the historical record. The globus pallidus interna rate theory was its replacement. The theory derived from increased knowledge of the anatomy and neurotransmitter chemistry of the basal ganglia. The cholinergic–​dopaminergic imbalance theory was not inconsistent with the new anatomical and neurotransmitter facts, but rather did not explain them. One cannot say that the cholinergic–​dopaminergic imbalance theory was inferior because it failed to explain all available facts. Indeed, the successor globus pallidus interna rate theory took no account of the clinical observations of the effects of anticholinergic medications. Thus, the cholinergic/​ dopaminergic imbalance theory was not subsumed by the globus pallidus interna rate theory. Perhaps, as suggested by Thomas Kuhn (1962) and Paul Feyerabend (1975), a fledgling science needs to ignore contrary evidence just to survive infancy. Even a mistaken theory can have utility, as discussed later. Advancing neuropharmacological research led to a concept of neurotransmitters as synonymous with the physiology (Valenstein, 2006). The neurotransmitters were transmuted incorrectly into dynamical terms of the day (and today) as excitatory and inhibitory. An inhibitory neurotransmitter, in many cases, causes hyperpolarization of the neuronal membrane, thereby reducing the probability of an action potential (the currency for exchange of information among neurons). The combination of anatomy and neurotransmitter chemistry led directly to the globus pallidus interna rate theory, where inhibition plays a major role, as discussed later. With the canonical forms of neurotransmitters in place, in terms of inhibitory and excitatory, and the canonical forms of the anatomy of the basal ganglia, coupled with the canonical forms of behaviors associated with pathology of the basal ganglia, the globus pallidus interna rate theory was born. The vast complexity of individual neurons was reduced to a canonical form, here called a macroneuron. The canonical forms of neurotransmitter function simplified the dynamics to a notion of contraries; that is, oppositional effects along a single dimension. The consequence was a theory whose nature is based on one-​dimensional push–​pull metaphysical presupposition. More importantly, the macroneuron became a means to reason regarding the operation of real neurons through the Fallacy of Pseudotransitivity in the form of the metaphor: real neurons are to real neuronal interactions as macroneurons are to their interactions. It also reflects the Fallacy of Composition. Figure 11.2 shows the “circuit or wiring diagram” of the basal ganglia–​ thalamic–​cortical system involved in control of movement. Such diagrams

Motor cortex

Str

Parkinson’s Disease

Motor cortex

VL GPe GPi

Str

STN SNpc

VL GPe GPi

Normal

STN SNpc

FIGURE 11.2 

Example of the globus pallidus interna rate theory based on the macroneuron approach. Major anatomical structures include the motor cortex, which is the source of the upper motor neurons, which project to the lower motor neurons in the brainstem and the spinal cord (not shown). The lower motor neurons project and drive muscles to produce movement. Other structures include the striatum (Str), which is a combination of the caudate nucleus and the putamen; globus pallidus externa (GPe); globus pallidus interna (GPi); subthalamic nucleus (STN); ventrolateral thalamus (VL); and substantia nigra pars compacta (SNpr). Each of these structures, containing many tens of thousands of neurons, is presented by a single neuron (macroneuron), and the operations of the basal ganglia–​thalamic–​cortical system are constructed based on how a single neuron (the macroneuron) would behave—​the macroneuron approach. There are two exceptions. There are two macroneurons because it is believed that there are two separate populations of neurons in the striatum that project differently: one to the globus pallidus interna (often referred to as the direct pathway) and one to the globus pallidus externa (referred to as the indirect pathway). Two macroneurons represent the substantia nigra pars compacta because it is thought that there are two populations of substantia nigra pars compacta neurons, each projecting to the two subpopulations in the striatum. The interconnections of the neurons between structures are color-​coded for their neurotransmitter action—​green as excitatory and red as inhibitory. However, this dichotomization is incorrect (see text).

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are far more than a synopsis of the anatomy—​they are a means of imputing function—​that is, physiology or dynamics—​and are an example of the anatomy-​ as-​physiology paradigm in neurology and neuroscience. Extrapolating physiology from the “circuit or wiring diagrams” involves the macroneuron approach. In this case, a single neuron is thought to represent each macroscopic structure. For example, a single macroneuron in the diagram (Figure 11.2) represents the hundreds of thousands of neurons that make up the globus pallidus interna, and the same process is carried out for all the other structures. The physiology or dynamics of the basal ganglia–​thalamic–​cortical system is hypothesized based on the properties of a single neuron, with the effects of neurons dichotomized as excitation or inhibition. The motor symptoms of Parkinson’s disease, particularly the loss of some movements and a slowing of other movements, are related to a loss of dopamine neurons in the substantia nigra pars compacta. The loss of dopamine macroneuron’s excitatory inputs onto the putamen macroneuron, which in turn projects to and inhibits the globus pallidus interna macroneuron, results in a loss of activity in the putamen macroneuron and consequently a loss of inhibition of the globus pallidus interna macroneuron. The consequent increased activity in the globus pallidus interna macroneuron inhibits and reduces the activity of the ventrolateral thalamic macroneuron. Because the ventrolateral thalamic neuron drives the motor cortical macroneuron, the reduction in activity in the ventrolateral thalamic neuron results in decreased activity in the motor cortical macroneuron, and, because the motor cortex macroneuron drives the motor neuron in the brainstem and spinal cord, the loss of drive by the motor cortex macroneuron causes less activity in the motor neurons that would drive the muscles to produce movement. The same sequential one-​dimensional process of inhibition–​excitation consequent to loss of the substantia nigra pars compacta macroneuron can be traced and similarly results in increased activity in the circuit that contains the globus pallidus externa macroneuron (Figure 11.2). Unfortunately, some, but by no means all, experimental studies demonstrated changes in actual neuronal activity that replicates what was predicted by the macroneuron approach. However, considerable evidence demonstrates that the changes in neuronal activities found were epiphenomenal and related to the manner in which the animal was rendered parkinsonian (Montgomery, 2007, 2012). Despite evidence that the changes predicted cannot be causal, and in view of other observations contrary to the theory, the globus pallidus interna rate theory continues in vogue—​a testament to human nature’s refractoriness of intuitive theories to contrary evidence (Johnson-​Laird, 2008). The case studied previously involved a number of reductions. Methodological reduction reduced the anatomical complexity of the basal ganglia–​thalamic–​ cortical system from millions of neurons to a few macroneurons. Theoretical reductions involve attributing neurotransmitters to one-​dimensional push–​pull

Necessary Presuppositions

inhibitory–​excitatory systems. Clearly, the reductionisms resulted in a failed theory that has demonstrated inconsistency with a large body of evidence (Montgomery, 2016). It is likely that the globus pallidus interna rate theory ultimately failed due to the loss of information according to the Second Law of Thermodynamics. Each reduction resulted in an irreversible loss of information, just as the mean or median (in this case analogous to the macroneuron) irretrievably loses information in a particular set of observations (Chapter 14). To be sure, the globus pallidus interna rate theory inspired many experiments and scientific careers. But, in the end, the loss of information meant the theory could not be adapted and had to be rejected wholesale. Unfortunately, many of the successor theories share the same reductions and one-​dimensional push–​ pull dynamics and thus likely will fail in time.

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The False Notion of Intention, Choice, and Inhibition Mother Nature, responding to the question from the audience “why did you create snowflake A instead of snowflake B?” said, “I inhibited snowflake B.” This Chapter extends the discussion of one-​dimensional push–​dynamics between two dichotomous states where intermediates are some admixture of the two dichotomous states. The metaphysical positions entailing the one-​dimensional push–​pull dynamics are most relevant to many neurological and psychiatric disorders. However, the concerns are also relevant to many current notions of genetic disorders that are viewed as either gain or loss of function abnormalities. The implications of a genetic metaphysics predicated on one-​ dimensional push–​pull dynamics likely will become increasingly pressing as genetic research attempts the reconstruction of dissected genetics into a reconstruction of the pathophysiology of disease.

One-​Dimensional Push–​Pull Dynamics One-​ dimensional push–​ pull dynamics is derived directly from Aristotle’s contraries. These dynamics and ontologies are readily apparent in Galenic medicine, with brief departures from humors to other entities such as expansion or contraction of blood vessels as argued by Benjamin Rush (1746–​1813; see Chapter 6). The germ theory did much to dissuade such one-​dimensional thinking, and further advances in medical science have continued to erode this mode of thinking. However, the presupposition of one-​dimensional push–​ pull dynamics continues unabated in neurology and psychiatry. Consequently, this chapter focuses primarily on neurological and psychiatric disorders. However, medical science outside of neurology and psychiatry still faces major challenges, as evidenced by the frequency of irreproducible medical research (see Chapter 15). Thus, critical discussions of neurology and psychiatry could 150

The False Notion of Intention, Choice, and Inhibition

lead to new conceptual approaches relevant to all fields of medical science, knowledge, and use. The witticism given at the introduction to this chapter suggests, again, a metaphysical presupposition of an involving continuum based on a dichotomization of extremes that are oppositional in their relations. The relative presence of one extreme suggests suppression of the other. As will be seen, neurology and psychiatry are still dominated by theories based on reciprocal functional relationships between various structures, with one inhibiting or being inhibited by others. Indeed, as will be seen, the neurophysiologist Sir Charles Sherrington created an elaborate hierarchical system of fundamental reflex mechanisms that are suppressed by so-​called higher centers. This notion continues to be a mainstay in motor neurophysiology. Subsequently, when interactions between neurons became synonymous with neurotransmitters, the reciprocal interactions were couched in terms of excitatory and inhibitory neurotransmitters. This is not accurate because, in many cases, applications of neurotransmitters may result in an early hyperpolarization that reduces action potential generation but is followed by rebound excitation that often results in net increased action potential generation. Certainly, there are exceptions, but those exceptions are revealing as well. For the most part, those systems least described by one-​dimensional push–​pull dynamics are those most directly tied to the environment. For example, much is known about the dynamics of the lower motor neuron in the spinal cord and how those neurons drive movement. Lower motor neurons directly drive depolarization of the muscle membrane, resulting in muscle contraction. The lower motor neurons depend on information from other sources, such as feedback from the sensory systems mediated by the spinal cord, and from other structures, such as the motor cortex wherein upper motor neurons reside. Considerable information is known about the motor cortex, which is directly connected to the lower motor neuron. Yet, one connection further—​that is, the basal ganglia, cerebellum, and associated motor cortical areas—​and virtually little if anything is known. It is impossible to trace a single coherent explanation from the neuronal activities in the basal ganglia to the specific recruitment of lower motor neurons to drive muscular contractions to produce behavior. One approach to this dilemma is to relieve the more central structures of the responsibility of controlling the ultimate driving of muscles, thus resulting in a theoretical reduction. For example, nearly every discussion of the role of the basal ganglia on movement is one of simple permission; that is, allowing or disallowing movement. As to the direct dynamics of the highly orchestrated muscle activities necessary to carry out a successful behavior, this does not seem to be a concern for the basal ganglia (however, this is inaccurate; see Montgomery, 2016). This follows from the hierarchical scheme of central nervous system motor neurophysiology.

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In motor neurophysiology, this scheme owes much to the work of Sir Charles Sherrington. His initial interest was in physiology of the motor cortex, but finding this too problematic or complex, he turned his attention initially to the motor nerves and then to their reflex connections in the spinal cord. However, to demonstrate those reflexes, Sherrington removed any influence from the more central structures—​meaning here less directly connected to the environment—​by dissecting the spinal cord and brainstem. This is an example of methodological reductionism initially from a utilitarian or practical concern. However, the methodological success bred a parallel theoretical reduction (the hierarchical organization) and, consequently, an ontological reduction, for example, into the pyramidal (those associated directly with the motor cortex) and extrapyramidal (those related to the basal ganglia and cerebellum) systems.

Methodological Reduction Creating New Ontology Methodological reductionism, where lesions are made in previously intact organisms, has been held suspect by the anti-​vivisectionists as far back as ancient Greece. These were epistemic rather than moral concerns. The epistemic concerns centered on the idea that the very act of dissection (methodological reductionism) destroyed the very explanations sought. To be sure, the phenomenon consequent to the dissection of the spinal cord and brainstem was different from the intact organism. Either of two general inferences can be held. Somehow the resultant and different behaviors relate in an inverse manner to the phenomenon thought lost—​the most common inference—​or that the lesioned organism is qualitatively different, and any use of the lesioned organism as a metaphor for the intact organism risks the Fallacy of Four Terms. If the latter is the case, and the lesioned organism is qualitatively different from the nonlesioned animal, then any inferences from the lesioned animal to the nonlesioned animal would be suspect. Note that this does not mean that the inference is false, only that the argument is invalid and hence the truth of the inference is indeterminate. The work of Sherrington is an example worth further discussion. Sherrington began with the simplest systems possible. Lesioning of the peripheral motor nerve resulted in paralysis. Hence an intact peripheral nerve is a necessary condition for motor function. Next, lesioning of the spinal cord just above the level of the relevant peripheral motor nerve did not produce paralysis but did produce abnormal motor behavior; hence, the peripheral nerve, remaining intact, is not a sufficient condition for normal behavior. The only type of behavior seen with lesioning just above the level of the peripheral motor nerve produced exaggerated deep tendon reflexes. With lesions progressively more cephalic, the deep tendon reflexes were increased but other reflexive and more complex behaviors were seen. This led Sherrington to suggest that the

The False Notion of Intention, Choice, and Inhibition

central nervous system is a hierarchical system where more intact cephalic structures shaped, by inhibition, the behaviors associated with the more caudal structures. Thus, the reflexes were the fundamental building blocks for motor behavior. As a result of the methodological reductions caused by transecting the central nervous system at different levels, a new ontology was created; that is, a unique entity of the specific reflexes. From that came a theoretical reduction resulting in a hierarchical system of reflexes. However, there is little evidence of its relevance when studying the intact organism, and, indeed, destroying the reflex arc, whether by dorsal rhizotomies or by anesthetics, appears to have little consequence on the subject’s movements. Certainly, movements in deafferented nonhumans appear normal (Taub et al., 1966; Terzuolo et al., 1974), although the fine structure of the movements, as evidenced by measurements of accompanying electromyographic behaviors, is abnormal (Terzuolo et  al., 1974). In humans, selective anesthesia of the gamma motor neurons does not appear to greatly disrupt movement in humans (Landau et al., 1960). Thus, it is unlikely that the various reflex arcs are the fundamental building blocks for normal motor control. The lesioned animals were a unique phenomenon with little relevance to the motor behaviors of intact organisms. To be sure, the anatomy necessary for the reflex, as demonstrated in the dissected subjects, is still present in the intact subject. Nonetheless, the presence does not necessarily mean that it plays a role in the behaviors that the reflex is used to explain or theorize. Certainly, in the dissected subject, the range of normal behaviors is not manifest, and thus, any clear relationship between those behaviors and the reflexes left behind is problematic. However, the default predisposition is to assume that there is a relevant dynamic, and the presence—​ and indeed exaggerated presence—​in the absence of connection to the more central structure is interpreted as reflecting a reciprocal and oppositional role to that of the more central structures. The same oppositional one-​dimensional push–​pull systems are inherent in the notion of executive function where the role of the frontal lobes, for example, is to suppress antisocial behavior. Unanswered is what particular structures are being inhibited that are the source of the antisocial behaviors. That there is some ontological entity that generated antisocial behaviors is thought evident by the appearance of the antisocial behaviors occurring with removal of the frontal lobes. An analogy is to the role of a librarian to keep salacious texts out of the hands of individuals as opposed to the individuals actually writing the salacious materials when the librarian is not there. Indeed, the very nature of investigational methods presupposes oppositional one-​dimensional push–​pull systems. Earlier in neuroscience, the attribution of function to a specific anatomy (functional anatomy) involved removing the anatomy and observing the behavioral consequences. Then the function of the removed anatomy was inferred from the behaviors remaining. A loss of behavior was interpreted as reflecting the function of the anatomy lost. However,

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any additional and particularly abnormal behavior was thought to be the release of other anatomical structures that generate the additional or abnormal behaviors. Thus, the dynamics of the experiments were one-​dimensional: the presence or absence of an anatomical structure and the presence or absence of some specific behavior. Modern-​ day anatomical–​ behavioral correlations typically involve neurometabolic imaging, such as functional magnetic resonance imaging, whose primary means of interpretation are increases or decreases in blood flow. Thus, the measure is a dichotomous variable, and increases or decreases (no changes in a structure are interpreted to mean that the structure is irrelevant) are one-​dimensional and oppositional (push–​pull); consequently, it is hard to see how any inferred theory of brain function could be anything but one-​dimensional push–​pull systems. To be sure, newer methods, such as the magnitude of coherence in fluctuations in blood flow, have introduced an analysis method that is not inherently one-​dimensional. The notion of the role of the frontal lobes to suppress antisocial behav­ ior presupposes some mode of existence for antisocial behavior even when not manifest because the frontal lobes are intact. Antisocial behavior is a potentiality that implies some ontological status (some version of reality). A potential antisocial behavior is not anything, according to the metaphysical presupposition. As will be seen, this metaphysical special status of potentiality harkens back to Aristotle.

Misperception of Inhibition Referring back to the conversation with Mother Nature, there is nothing in the laws of physics that would give snowflake A a greater chance of being born than snowflake B or any laws that would favor snowflake B. However, snowflake A was created and snowflake B was not. It is a huge anthropomorphism to suggest that there was some choice in the matter whether snowflake A or snowflake B was created. Yet, humans are highly prone to post-​hoc anthropomorphisms when examining phenomena. In studying split-​brain patients, Roger Sperry would flash instructions in the left hemi-​visual field that the subject would perform. The instructions were not accessible to the left hemisphere so there was no way the subject could report verbally what the instructions were. Nevertheless, the subject made up a complete description and explanation as though he had read the instructions. Humans are capable of self-​observation, and the need to self-​explain is powerful. In situations where choice among potentials is possible, humans infer intents and generalize these intents to other phenomena. Hence, one attributes intent to Mother Nature and explains the appearance of snowflake A rather than snowflake B as something she intended. Intent implies options, options

The False Notion of Intention, Choice, and Inhibition

imply choice, and choice means championing one option over another, thereby inhibiting (vanquishing) the option not realized. Modern physicists would argue that the formation of snowflakes is a self-​organizing complexity. Molecules of water just come together to form a snowflake. Even though the laws of physics governing snowflake formation are exactly the same for every snowflake, tiny and perhaps imperceptible variations in the properties of the water and the atmosphere are amplified to produce an incredible, potentially infinite, number of different snowflakes. Another example is self-​organization among a group of hungry puppies. As shown in Figure 12.1, puppies eagerly await milk being placed in a bowl. The puppies push their way toward the bowl and, in the process, push against each other. Their forward motion becomes a pinwheel motion in a clockwise direction. The puppies could have easily gone in the opposite direction, but does that mean that the direction not chosen (counterclockwise rotation) was “inhibited?” Many complex systems can evolve into discrete states, such as puppies going in a clockwise or counterclockwise direction. The state to which

FIGURE 12.1 

Self-​organization in puppies. (A) A group of puppies is given milk. Initially, the puppies appear distributed randomly around the saucer (A and B). As the puppies push forward to obtain the milk, they begin moving as a rotating pinwheel, in this case clockwise (C). The puppies only know to push forward toward the milk and do not communicate their intentions to each other. However, the saucer is more like a circular trough with a center without milk (D). From Perony, N. Puppies! Now that I’ve got your attention, complexity theory; https://​www.youtube.com/​ watch?v=0Y8-​IzP01lw.

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the system evolves is unpredictable. Importantly, evolution to a specific state is highly dependent on initial conditions, and an ever-​so-​slight difference in the starting point of a complex system changes greatly when the final state is reached. Thus, a slight difference, perhaps imperceptible, in atmospheric conditions led to snowflake A instead of snowflake B, and a slight difference in the puppies’ initial position relative to the dish could have led to direction A versus direction B. If one could know the initial atmospheric conditions that led to snowflakes A and B, then clearly whether snowflake A was formed has nothing to do with whether snowflake B would have been formed, and the converse is true as well. The eventual formation of snowflake A would not seem to be the consequence of the inhibition of snowflake B. But what if the differences in the initial conditions were not observable? In that case, it would appear that snowflakes A and B arise from the same initial conditions. Having the same initial conditions, the natural intuition is that there must be some form of choice in order for snowflake A instead of snowflake B to have been formed. The notion of choice, hence intention, is only because the initial conditions were unknown, and one thought—​erroneously—​that they were the same. An additional factor is the peculiar notion of potentiality. Implicit in the question asked of Mother Nature is the presupposition of the potential for snowflake B. The potential for snowflake A is clear because the potential for snowflake A is demonstrated in the realization (actualization) of snowflake A. Because snowflake A exists, it thus has the ontological status of a fact. But what is the ontological status of snowflake B, which, although not existing, at least has the potential to exist? Clearly, snowflake B has some sort of ontological status, even if this is only a type of proto-​actuality or proto-​fact of reality. If the ontological status of snowflake B is at least something, then it has some form of existence, and, if so, the ultimate creation of snowflake A would be seen as a choice over the potential existence of snowflake B. The modern physicist would argue that an argument for the potential of snowflake B that has some ontological status is nonsense. Snowflake B does not exist in any manner until it actually exists. To impute some mode of existence prior to existence is to create a misconception. An analogy is quantum super­ imposition, whereby a subatomic entity has both wave and particle properties. It is not until one looks for either the wave or the particle properties that the wave or particle properties exist (see Schrӧdinger’s cat thought experiment in Trimmer, 1980). Before one actually tries to measure it, the entity is both a wave and a particle, or, in other words, the particle-​ness and the wave-​ness are superimposed on the same entity. One could consider that the state of snow­ flake A is superimposed on the state of snowflake B and that it is not until the beginning of the process that either snowflake A or snowflake B emerges. Snowflake A does not inhibit snowflake B any more than wave-​ness inhibits the particle-​ness of a subatomic entity.

The False Notion of Intention, Choice, and Inhibition

It is a mistake to think that the wave–​particle duality of a subatomic entity is some admixture of the potential for a wave and the potential for a particle. The problem is that such superimposition is extremely counterintuitive, as is much of quantum physics, which even flummoxed Albert Einstein. In many ways, the common notion of the ontological status of potentiality, such as the potential for a wave and the potential of a particle or the potential for snowflake A and the potential for snowflake B, dates back at least to Aristotle (385–​322 bce). Aristotle, as all who came before and after him, was greatly concerned about change. Why did an acorn become a tree and not a frog? Why did lesioning of the frontal lobes result in a specific character not observed with lesioning of other parts of the brain? There must be something in an acorn such that it would become a tree rather than a frog. However, when looking at an acorn, one does not find any semblance of a tree. In many ways, Aristotle’s notion of change still influences modern thinking. He asked the same questions being asked today, and so it is not surprising that his answers would find resonance today.

Potentiality How does one reconcile that an acorn becomes a tree and not a frog when there is nothing in the acorn itself that looks anything remotely like a tree? Aristotle’s answer was that the acorn contains the potential of a tree. What other answer is possible? If the acorn does not contain the potential of a tree then by what external force or magic does the acorn become a tree? (Interestingly, vitalists argued for some outside life force that imposed on the acorn the necessity of becoming a tree.) A modern view would say that the potential for a tree lies in the DNA of the acorn, but is this really different from Aristotle’s notion that the acorn contains the potential of a tree? That an acorn has the potential of becoming a tree and not the potential of becoming a frog leads to the notion that the potential is real or has some ontological status. If it were not real and thus had no ontological status, how is it that the acorn never becomes anything but a tree? Aristotle rejected the notion of some magical external force suddenly making the acorn into a tree. In his physics, Aristotle wrote “What is, cannot come to be.” Aristotle is saying that the tree must have always existed, if only in a potential form within the acorn. A tree does not form spontaneously out of a void. Aristotle also wrote “nothing can come to be from what is not.” Who today, other than those conversant with complexity theory and quantum physics, would say that Aristotle’s position is unreasonable? Actually, it is quite intuitive and quite appealing. Thus, potentiality is not nothing; it has some ontological status, which makes it seem a thing. Thus, the potential of snowflake B is real, and that snowflake A is what was created means that the potential of snowflake B must be denied in

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an active sense, hence inhibited. Similarly, the potential for antisocial behavior is not anything, in which case its absence can only mean that it is suppressed from being. Somehow a human can make any number of choices of actions at any moment in time. A human can choose to reach for a cup or to scratch her nose. Something must control which action will be executed and which will not. But what is the ontological status of possible actions so that one can be allowed and another suppressed? The possible actions could be analogous to Aristotle’s notion of potential, which he referred to by the ancient Greek word dunamis (Witt, 2003). Aristotle distinguished between potential as a chance occurrence versus a non-​chance occurrence. The potential to reach for a cup would not be the result of chance. But what, then, is the nature of potential that is not chance? Aristotle argues that the potential that is not chance reflects in some manner the nature of the object that would manifest the potential. Thus, at least since Aristotle, the potential has had a unique ontological status. The brain, specifically the globus pallidus interna, generates manifest actions. Therefore, the function of the brain cells, particularly the neurons in the globus pallidus interna, must generate manifest actions. However, actions may be potential; for example, the action to scratch one’s nose is not engaged, while the person reaching for a cup is the actual engaged action. The potential action, scratching one’s nose in the example just given, has an ontological status or a type of existence, following from Aristotle, that must be accounted for in the brain, particularly the globus pallidus interna. Therefore, the neurons in the brain, particularly the globus pallidus interna, must address not only the actual action but also the simultaneous potential actions. This formulation inevitably leads to the notion that the brain, particularly the globus pallidus interna, must act to select that which will be actual from all that is potential. Clearly, Aristotle’s notions still operate today in understanding the function of the basal ganglia, particularly the globus pallidus interna. There are alternatives, based on complex systems, that do not need to postulate ontological potential actions; instead, there is not a choice but a self-​organization of chaotic and complex systems, as discussed in more detail in Chapter 14. Clearly, potential actions have an ontological status to many, but this poses a significant problem. It is estimated that the human globus pallidus interna contains approximately 300,000 neurons. There are at least that many (and probably more) potential actions (just think about reaching for a cup from many different starting points). The number of potential actions exceeds the number of neurons that would select or deselect an action. One could argue that each neuron controls several potential actions; for example, neuron A could control actions X, Y, and Z, but this would not resolve the problem. How would the brain then select among actions X, Y, and Z if it is up to a single neuron? It is astonishing that modern neurophysiology has yet to even consider this problem.

The False Notion of Intention, Choice, and Inhibition

Like all humans, Aristotle was looking for explanations, particularly explanations that were relatively understandable. As is the case today, humans are basically asking the same questions as Aristotle did, and it is not surprising that one finds the same approach to answering those questions quite appealing, even reasonable. Like every other human, Aristotle was confronted with the enormous complexity and variety of natural phenomena. In response to such diversity, there really are only two approaches to understanding natural phenomena: either there are as many explanations as there are phenomena (a most unattractive option), or there is some economical set of underlying principles that, in various combinations, explains all the diversity of natural phenomena. The latter would seem more appealing.

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The Role of Metaphor

The patient has a disabling cerebellar outflow tremor secondary to hyperporcelinemia, which is very rare. We should provide deep brain stimulation (DBS) of the ventral intermediate nucleus of the thalamus because it works for other cerebellar outflow tremors. PHYSICIAN: DBS has only been approved for Parkinson’s disease and essential tremor. There have not been any prospective randomized control clinical trials of DBS for hyperporcelinemia. RESIDENT: There are not enough patients with hyperporcelinemia to conduct a prospective clinical trial. Certainly, the cases published where DBS helped patients with tremor due to multiple sclerosis, post-​ anoxic injury, posttraumatic injury, and galactosemia should provide justification for at least trying because there is little else to offer. PHYSICIAN: Well, then, we will never know; we are not doing surgery. RESIDENT (to herself): I’ll get another opinion. RESIDENT:

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The centrality of hypothesis generation to medical decisions was discussed in Chapter 10. The centrality of hypotheses is seen in the common use of abduction, which is of the logical form if a implies b is true and b is true then a is true. In this case, a is the hypothesis and b is the prediction generated from positing the hypothesis as true. Thus, a medical decision is only as good as the hypoth­ esis that shapes the question whose answer drives the decision. Chapter  13 introduced the role of metaphor (the Fallacy of Pseudotransitivity) as a potent hypothesis generator. Indeed, every time a clinician interpolates or extrapolates findings from any clinical study to an individual patient, the clinician is engaging in metaphor. Similarly, metaphor is critical to the use of approved treatments for off-​label uses, as described in the hypothetical case at the beginning of this chapter. For example, Mary Walker (1888–​1974) generated the hypothesis anticholinesterases may improve the weakness of myasthenia gravis, as anticholinesterases improve the weakness of curare. This hypothesis is an

The Role of Metaphor

example of a metaphor or the Fallacy of Pseudotransitivity. When the hypoth­ esis was subjected to clinical testing, anticholinesterases proved beneficial to patients with myasthenia gravis. This chapter explores the nature of metaphor in medical reasoning and science in more detail. Metaphor in this context is extended from metaphor in linguistics and literature. Metaphor is a device used to explicate vague or abstract terms (target domain) based on a transference of meaning from a more readily understood term (source domain). In the case of anticholinesterases may improve the weakness of myasthenia gravis, as anticholinesterases improve the weakness of curare, the uncertain term (target domain) anticholinesterases may improve the weakness of myasthenia finds support in the source domain anticholinesterases improve the weakness of curare. This transference is based on some implicit or explicit similarity between the target and the source. While there are many forms of metaphor (Kövecses, 2010), the account of metaphor in medical decision-​making and science will be extended to the notion of a procedure (verb), rather than a noun. This is termed a process metaphor, and this notion is a significant departure from more traditional linguistic and literary notions of metaphor. Communication and everyday discourse would be nearly impossible without metaphor (Kövecses, 2010). Nearly all transcendental notions, which tend to be the most important, require some metaphor to convey meaning. Pangs of love are often explicated as hunger and life in terms of journeys. Indeed, Kövecses argues that many important metaphors succeed by using bodily experiences as the source domain. It would be very difficult indeed to talk meaningfully about love, friendship, patriotism, faith, death, life, and many other important things without the use of metaphors. Even language itself is a metaphor, as unspoken thoughts are the target domains that require symbolic language as the source domain. The same likely is true for scientific thoughts. Kövecses defines metaphors as understanding one conceptual domain in terms of another domain—​clearly an issue of epistemology. Although a general definition, there is “structure” in metaphors that can be translated into operating principles.

Metaphors in Medical and Scientific Knowledge and Understanding Epistemic risk is important to the effective use of metaphor. No epistemic risk follows when the target and source domains are identical, a tautology such as the identity axioms of mathematics where a = a. No conceptual work can be accomplished. However, if the target and source domains are completely different—​that is, without any conceptual, semantic, syntactic, or linguistic similarity—​the epistemic risk is too great, and it is entirely chance whether any

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work can be done. Furthermore, a greater epistemic risk creates a greater risk for the Fallacy of Four Terms (Chapter 15). For example, using studies of a rodent as a metaphor to humans is a greater epistemic distance than using nonhuman primates as a metaphor to humans, in some circumstances. Studies of cognition in a rodent as a metaphor to human cognition has greater epistemic degrees of freedom compared to studies of glucose metabolism. For metaphors to work—​generate hypotheses potentially leading to new knowledge or understanding—​the essentials lay in those parts of the source and target domains not identical to each other. Furthermore, there must be some way for the parts of the source domain not identical to the target domain to influence, or bleed into, the target domain (note the metaphorical use of “bleed into”). Knowledge of understanding of the target is enlarged or enhanced by those aspects of the source domain that are not identical but that have bled into the target domain. Mapping between the identical components forms the bridge for the nonidentical components to bleed into the target (note the mixed metaphors of “bridge” and “bleed into”). Considering metaphors as syllogisms, it is the bridging term between the major and the minor terms that allows the major and minor terms to be linked or that the minor term can be “bled into” the major term. For the syllogism to do work, the bridging terms must be different but not too different. In the example of anticholinesterases, curare poisoning, and myasthenia gravis, the bridge was weakness that allowed the concept of anticholinesterases for curare poisoning to “bleed into” myasthenia gravis. However, the bridging term is the weakness noted in both curare poisoning and myasthenia gravis. The nature of the weakness was just different enough to generate reasonable hypotheses. Note that the metaphor would not have worked if weakness secondary to a stroke or spinal cord damage was the target domain: here, the epistemic distance and degrees of freedom would be too great.

Metaphors Leading to Errors in Medical and Scientific Knowledge and Understanding A judicious use of metaphors in medicine and science requires an explicit or implicit judgment on whether to admit the nonidentical components from the source into the target domain. However, often the nonidentical components of the source domain sneak into the target domain. An example is the early theory that DBS causes a decrease in neuronal activity in the stimulated target. In this case, surgically destructive (ablative) lesions of the globus pallidus interna, called pallidotomies, were used as a metaphor for DBS of the globus pallidus interna, and similarly for thalamotomy and DBS of the ventrolateral thalamus. In both cases, similarities between the target domain (DBS of the globus pallidus interna and the ventrolateral thalamus) and the source domain

The Role of Metaphor

(pallidotomy and thalamotomy) were “linked” by similarities in the clinical benefits—​ considered the identical elements between the source and target domains. The nonidentical elements, destroyed brain in the pallidotomies and thalamotomies in the source domains, bled into the brain electrical stimulation of the target domains. Reconciliation required that the physiological effect of the nonidentical elements be the same; that is, a loss of neuronal activities. This is wrong (Montgomery, 2012). Consider this metaphor: a stroke involving the motor cortex of the brain and curare both produce paralysis. However, it would be an error to say that both a stroke and curare produced paralysis by the same mechanism. In defense of the incorrectly attributed suppression of neuronal activity in the target of DBS, the authors of the metaphor were primed to think that way by the metaphysical predispositions derived from the ancient Greeks to today (see Chapters 11 and 12). Metonymies, like metaphors, link different concepts, such as “He is the head.” In this case, “He” is somewhat ambiguous, at least as far as his function, but this is clarified by reference to the “head.” In this case, “head” provides a notion of leadership to “He” in a manner similar to the head leading the body. Although “He” is not just a “head,” the metonymy “He is the entire body” would not work. The function of the “head” is isolated from the body. This is an example of the part–​whole metonymy in which part of an object is used to identify or substitute for the whole. Such part–​whole metonymies are rampant in reductionist medical theories. These part–​whole metonymies risk the Mereological Fallacy (see Chapters 11 and 12).

Metaphors and Metonymies as Structuring Observation The discussion of metaphors and metonymies given earlier has been in the context of language and their linguistic functions. Those functions are a mapping of meaning inherent in the source domain onto the target domain. Metaphors and metonymies, in their linguistic sense, are post-​hoc, as revealed when the question becomes “where did a specific metaphor or metonymy come from?” Did the author have a notion of the target and then search for the appropriate source domain? Did the author have a source domain in mind prior to formulating a target domain? Some winnowing of potential source and target domains is necessary for construction of the metaphor, thus risking the a priori problem for induction. Perhaps it was the macroneuron approach presupposition that enabled the anatomical architecture to be “observed” as a physiological theory, as discussed in Chapter 11. Similarly, the capacity to “observe” is mirrored by the capacity “not to observe”; for example, “no data” is transformed to negative or countervailing data. A failure “not to observe” may be driven by presuppositions, such as those inherent in rationalist/​allopathic medicine.

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Nonlinguistic Metaphors A unique form of a nonlinguistic metaphor is a process, although of a specific kind. The process is unending and hence cannot be completely instantiated in linguistic terms. Rather, the process “points to” or converges on a specific new hypothesis or knowledge claim. One readily admits to knowing the set of all counting (integer) numbers, such as 1, 2, 3. . . . However, the set of all counting numbers is infinite; thus, it is not possible to actually “know” every counting number (the philosophical notion of actual infinity). However, that knowledge could be constructed by simply adding the value 1 to any number conceived (the philosophical notion of potential infinity). Thus, knowledge of the set of all counting numbers derives not from a full observation of the set of all counting numbers, but rather from the process by which the set is conceived—​the process of adding 1 to any conceived number. Thus, the target domain of the relevant metaphor is the set of all counting numbers, and the source domain is the process of adding 1’s to any number conceived. This is a process metaphor. Galileo’s demonstration of inertia using inclined planes is a process metaphor. A ball is placed on an inclined plane and is allowed to roll down one inclined plane and then up another inclined plane. It turns out that the ball rolls up to the same height on the second inclined plane as the height from which the ball was released on the first inclined plane. The next step is to progressively decrease the angle of the second inclined plane, and the ball has to roll farther to reach the height at which it was released. The process metaphor holds that if the angle of the second inclined plane was reduced to zero, then the ball would roll forever because it could never reach the height from which it was released. Archimedes’s approach to determining the area of a circle starts by constructing a series of polygons within a circle. The polygons can be broken into a series of triangles whose areas are determined readily. As the number of sides of the polygon increases, the area covered by the polygon becomes closer and closer to that of the circle, although never actually equaling the area of the circle. However, with a polygon of sufficient number of sides, the area of the polygon can be thought to approach the actual area of the circle, and, as the limit of the number of sides approaches infinity, the area of the polygon can be said to equal the area of the circle. Newton took a very similar approach to his first construction of his law of gravitation. He imagined an object bouncing around in a square box, measuring the directions of the bounced movements. He then increased the number of sides of the box progressively until it approximated that of a sphere and the gravitational law became the limit as the number of sides of the box approached infinity (Newton, 1999). One could argue that the squaring of a circle (Archimedes) or the reverse (Newton) is contained in the linguistic metaphor that “the area of the circle is an infinite sided polygon,” but this would be vacuous in terms of concrete

The Role of Metaphor

language based on understanding of the term “infinity.” Rather, understanding derives from the repeated process of increasing the number of sides in the inscribed polygon in the limit as the number of sides approaches infinity. One never arrives at the end of the process, although the results of the process are understood. Consider the example of instantaneous velocity of a moving object. Traditionally, the velocity of an object is determined from the distance traveled in an interval of time. For example, an object traversing 1 mile during the course of 1 hour is determined to be traveling at 1 mile per hour. As the moving object is observed, it is quite reasonable to conclude that it is moving at each instant in time. However, instantaneous velocity presupposes a time interval of zero. Any value or number divided by zero is an infinite number (or not a number computationally); yet, clearly, the object is not observed to be traveling with an infinite velocity or traveling at “not a velocity.” The approach to this paradox would be to measure the distance traveled over progressively smaller time intervals. As the time intervals are not zero, the paradox of an infinite velocity is avoided. Lebiniz’s great intuition, and also Newton’s, was to consider and formalize the notion of what would happen as the time interval approached zero, but not actually zero. Instantaneous velocity is the velocity determined by the distance divided by an interval that is so close to zero that it is considered for all intents and purposes to be zero. Velocity and other phenomena can be described as the function y dependent on some value x (some distance traveled, for example) and t, which represents some interval of time. Instantaneous velocity is determined as the time interval approaches zero, represented by dt, and the corresponding distance dx. The velocity becomes dx/​dt. This is the basis of calculus, and remarkable advances in the scientific enterprise since are owed to this notion of limits that cannot be explicated directly and solely in language or symbols. Although it can be expressed as language or symbols, such as dx/​dt (one representation of the first derivative), it can only be understood as a process. The Taylor expansion series can be considered a process metaphor. Consider any phenomenon changing over time. One can measure some aspect of the phenomenon at each point in time and write an equation or function that describes that phenomenon, such as f(x). However, the function may be very complex and difficult to solve or understand. However, the function, f(x) can be equated to a sum of a series of terms created from successive differentials of the original function, such as f ´(x), first derivative, f ´´ (x), second derivative, f ´´´ (x), third derivative, and so on. Thus, f(x) can be approximated as f ( x ) ∝ f ′ ( x ) + f ′′ ( x ) + f ′′′ ( x ) + …

The successive derivatives may be more understandable or solvable as each represents a type of simplification for the original function derivative.

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However, it is only in the limit of an infinite series that the approximation or the sum equals the original function. In practice, an infinite series is never practical but some reasonable series can be used. Again, the understanding of the function is a consequence of the process and not the actual instantiation.

Reductionism as a Process Metaphor Consider the situation of taking a watch apart. In this sense, a reductionist approach would disassemble the watch into its components. That the operations of the watch can be fully appreciated from the components is demonstrated when the components are reassembled and the watch functions. It is not surprising at all that the watch would function and that the role each component plays in the watch function is easily inferred. The richer sense of reductionism is when not all the components and their relationships to each other are known from the process of deconstructing the watch. For example, imagine giving another person, with no knowledge of a watch, a pile of dissembled parts. What is lost is the relationship between the parts—​an irreversible loss of information according to the Second Law of Thermodynamics as Applied to Information. Now the process is rather like putting a jigsaw puzzle together. The real value of reductionism is how few isolated and unrelated parts still allow the other person to construct a theory of watch function. The puzzle or the process of puzzle-​solving becomes the metaphor (see Haack, 2003). Again, consider a jigsaw puzzle. Typically, one begins to assemble the pieces that have a straight edge as these are likely to form the border of the puzzle. This is precisely the approach Sir Charles Sherrington employed in order to understand the motor function of the central nervous system. Initially, he started investigating the motor cortex but found it be underdetermined (insufficient knowledge to make reasonable hypotheses). He subsequently changed to studying the peripheral nerve and spinal cord as the starting point. He subsequently progressed in a hierarchical fashion through the brainstem and ultimately to the motor cortex. Like starting with jigsaw puzzles with straight edges, starting with the peripheral nerve and spinal cord was the most tractable.

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The patient died about an hour ago on the hospital floor from a myocardial infarction. What changes can we see in the body? PATHOLOGIST: In that case we may not see any changes; for example, changes that could be seen on electron microscopy may not be seen for 2 to 3 hours after death. STUDENT: Then what anatomical or pathological changes occurred at the precise moment of a patient’s death? Certainly, any changes occurring 2 to 3 hours later could only be said to be the consequence of death but not the cause of death. PATHOLOGIST: We can see that the patient has obstruction of the coronary arteries that led to the cessation of blood flow to the heart that then led to damage to the muscles and, consequently, failure of the heart to pump. STUDENT: I can see where this is a nice post-​hoc explanation but I am mindful of the logical fallacy of post-​hoc ergo propter hoc. Can we see changes in the heart muscle to prove you are correct? Because the patient died only an hour ago, you said we might not see any abnormalities in the heart muscle so how do we know that damage to the heart muscle was the cause of death? PATHOLOGIST: Well, you saw the patient just at the time of death, and you observed there was a loss of heart sounds, meaning that there was no blood flow so the heart was not pumping. STUDENT: Well, I guess we can say that the first instance of death is the failure to move, in this case moving blood, but also the patient did not move when I tried to rouse her and her chest wall did not move. I guess from Newtonian physics that must have been some loss of energy, such as kinetic energy, whose loss occasioned death. PATHOLOGIST: That seems reasonable. STUDENT:

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Professor, you are not saying that the loss of some sort of energy is the sine qua non of death? Wouldn’t that mean you are a vitalist?

STUDENT:

At the very least, evidence of life is dynamics—​motion and change. This chapter expands on previous discussions of dynamics and its implications for the science and practice of medicine. The question is how to understand and measure change and that which produces change. The ancient Greeks held that examination of the static structure of the organism—​that is, anatomy—​would provide such an explanation. For this reason, many scholars opposed human or animal dissections. Similarly, the ancients doubted that study of wounded animals or humans or vivisection of living animals or humans would be useful as the act of vivisection distorts exactly what it is attempting to understand (Zaner, 1998). For example, Sir Charles Sherrington’s (1857–​1952) “reduced preparations” of spinal cord lesioned animals do not apply to intact humans (see later discussion and Chapter 12). The rationalist/​allopathic physicians’ conceptual predispositions would drive adoption of reductionist science (accepting anatomy and vivisection, even if not exactly experimental) and the empirics would resist.

The Necessity of Addressing Dynamics, but How? Given the skepticism of static sciences, for example anatomy, efforts were taken to understand the forces that give rise to the dynamics of living things. For Aristotle, the fundamental static components of the world, the four elements—​ earth, fire, water, and air—​were not sufficient to generate life, and pneuma (vital heat) was necessary. While pneuma is the force or energy, the direction or consequence of force is the cause. Yet how is one to understand force or energy, or pneuma, for example, critical to understanding the living from the static or unchanging constituents? Traditionally, force or energy is defined by the capacity to perform work, such as moving an object from one location to another—​kinetic energy. Yet the kinetic energy is not observed; only the object moving from one location to another is observed. Thus, the great challenge is to infer the forces or energies by the changing relations of what can be seen—​static objects, such as parts of a living organism. The challenge then is to infer the forces, never seen, from the changes in the static object, which are seen. Furthermore, the changes in living organisms, for the most part, are not random because that would be incompatible with life. How does one account for the nonrandom change seen in the observable objects? Aristotle noted an efficient or moving cause, where an artificer utilizes force(s) to create or change a substance, such as building a house from pieces of wood, brick, or stone, with these being the material causes. The final cause

Dynamics

or telos, is the purpose for which the materials were organized; for example, a house. A  force or set of nonrandom interacting forces assembles the same object (categorically), a house. Yet the house is not derivative of the component pieces of wood, brick, or stone as any number of other things can be constructed from the components. It is easy to understand why Aristotle and many others since would think that there is a force that drives the building of a house that is independent of any force that is responsible for the pieces of wood, brick, or stone. That force is telos or purpose, and it is the final cause. The series of causes in Aristotle’s conception of causes include material (the pieces of wood, brick, or stones), to the formal (the arrangement of the components), to the moving (the house builder), and to the final (house) cause. Furthermore, Aristotle and many others argue that each cause or change requires some prior cause or change (Principle of Sufficient Reason). This demands an answer to the question “What is the first cause, the ‘prime mover?’ ” The Principle of Sufficient Reason, combined with the Principle of Causational Synonymy, while logically sound, creates a perhaps unresolvable conundrum of not being able to discover the prime mover. For example, changes in neurometabolism as reflected in functional magnetic resonance imaging of the brain associated with a particular behavior are not causally synonymous with an actual mechanism causing the behavior. However, the extraordinary difficulty of actually observing the fundamental causes (changes in electrical potential across the cell membrane of excitable cells) tempts one to stop at some other level of analysis (changes in metabolism), thereby introducing epistemic distance and degrees of freedom, leading to the Fallacy of Four Terms (Chapters 10 and 12). Reasoning from neurometabolic to electrical changes is difficult at best, and there is a tendency to stop at the level of neurometabolic changes. However, the risk then is to equate the neurometabolic changes as the prime mover, thus conveying a wrong sense of the cause. The vitalists argued for some source of energy, independent of the static entities, that induced and continued movement and change, thereby sidestepping the issue of the prime mover and, at the same time, providing a direction or “intent.” Given the relatively crude state of reductionist materialist science, the vitalists’ position appeared strong. Even Ernst Mayr (1904–​2005), one of the leaders of modern evolutionary biology, wrote of vitalism It would be ahistorical to ridicule Vitalists. When one reads the writings of one of the leading Vitalists like Driesch one is forced to agree with him that many of the basic problems of biology simply cannot be solved by a philosophy as that of Descartes, in which the organism is simply considered a machine. . . . The logic of the critique of the Vitalists was impeccable. (Mayr, 2002) The rationalist/​allopathic physician may have believed that the emergence of histopathology, coupled with improved physical diagnosis, particularly in

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the hands of William Osler (1849–​1919), countered the vitalists’ position by providing a strictly materialistic account of human health and disease. It is not clear that this has been entirely successful.

Continuing Concerns for Telos Sherrington used such teleological argumentation to explain the progressive increase in complexity of behaviors, often associated with the need for increasing complex sensory input, to higher nervous centers located toward or in the head. This was the case, Sherrington argued, because the head was the first part of the body that would encounter a new environment, at least for a quadruped (Sherrington, 1906). From Chapter  11, the cell theory describes the cell as the fundamental functional unit of the organism. At one level of analysis, the description of the cell is structural or anatomical. However, the function of the cell is one of attribution. In other words, the function of the cell did not arise from first considerations of the cell alone. At least initially, there was nothing unique about a lymphocyte as opposed to a hepatocyte that immediately indicated the role of a lymphocyte in immunity. The function of the cell was a microversion of the tissue or organ in which the cell was imbedded. The myocyte is embedded in the muscle whose function is to contract; consequently, the function of the myocyte is to contract. The telos of the cell was to fulfill the purpose of the organism, via cells, then tissues, and then organs, although the discovery of function likely took the opposite direction. The function of the organism was attributed first to the organ, then to the tissue, and finally to the cell. This approach of attribution of function becomes more problematic with more complex functions, tempting anthropomorphisms particularly invoking intent—​a form of telos. For example, the intent of the frontal lobe is to inhibit antisocial behaviors or of the globus pallidus interna to prevent unintended movements (Chapter 12). The result is anthropomorphism, where the dimensions and dynamics are in described terms of human intention or predilection. A classic example was phrenology (Figure 14.1), developed by Franz Gall (1758–​1828). This method of attribution to parts of anthropomorphic functions or dynamics continues. The phrenology approach is not so distant from neurometabolic imaging of human functions, particularly related to cognitive functions. With the movement from the brain and electrically based physiology to neurotransmitter-​based physiology, human functions are attributed to chemicals such as dopamine and norepinephrine in depression or oxytocin in social versus antisocial behavior. This latter approach is not so distant conceptually from Galen’s humors (Arikha, 2007).

Dynamics

FIGURE 14.1  Attribution

of anthropomorphic functions onto the physical substrate of the brain. The structure of the brain was thought to influence the shape and contours of the skull. Thus, by measuring the shape and contours of the skull, the phrenologist inferred the functions and capacities of the underlying brain.

One-​Dimensional Push–​Pull Dynamics Most dynamics fell to being one-​dimensional push–​pull systems, which can be appropriate. More often, one-​ dimensional push–​ pull dynamics are implicitly presumed or explicitly assumed as a means to render analysis of the phenomenon tractable. For example, prior to the advent of computers of sufficient power, it was nearly impossible to understand the dynamics of chemical reactions as they occurred. Consequently, most chemical reactions were studied

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after they were allowed to come to equilibrium and steady states (also referred to as quasi-​static or metastable states), where the dynamics of the reaction in one direction are counterbalanced by equal dynamics of the reaction in the opposite direction, typically through the same medium (Chapter 11). As will be seen later in this chapter, the situation is quite different in chaotic and complex systems. An example of the reversible chemical reaction where chemical A combines with chemical B to produce chemical AB. This is allowed to reach equilibrium or steady state, where the concentrations of chemicals A, B, and AB are constant or static. The danger is thinking that, at equilibrium or steady state, A, B, and AB are not changing or are static (discussed in more detail in Chapter 11). What appears static or unchanging, “on” or “off,” one state or a competitor state, should not infer that the dynamics are similarly simple and dichotomous. Consider the Necker cube visual illusion described in Chapter 9, where the shaded surface appears at the front and at other times at the back of the figure. There are no perceptible transitions between the two states—​front and back. Furthermore, one does not simultaneously see the shaded surface as both front and back (Figure 14.2). Had there been some sense of transitions between the two states, the illusion may be more understandable. The sudden translation of the shaded area is called a bifurcation, which argues for a dynamic mechanism underlying the perception of the Necker cube. Even though the perception appears stable, the underlying mechanisms are not stable.

FIGURE 14.2  The

Necker cube illusion and an example of bifurcation between two states given the same initial conditions. The cube appears as a three-​dimensional object with transparent sides. Some will view the shaded surface to the plane in the back. Others will see it as the plane in the front. Many will see the position appear to shift instantaneously between the front and the back; in other words, the perception of the shaded surface bifurcates between a perception of the shaded surface on the front or on the back. The physical appearance of the image is exactly the same regardless of whether the shaded surface is in the front or the back.

Dynamics

Understanding Dynamics Through Metaphor The metaphor of political theory led to the cell theory, and its claims are related to physiology (essentially dynamics) (Chapters 11 and 12). Another metaphor was neurotransmitters slow the heart as electrical stimulation of the vagus nerve slows the heart; therefore, the vagal nerve effect must be mediated by a neurotransmitter. Furthermore, application of a neurotransmitter is to physi­ ology (of the heart) as electrical activity is to the physiology (of the heart), hence neurotransmitters and physiology are synonymous (Valenstein, 2006). However, the notion of slowing the heart with a neurotransmitter or the application of electrical stimulation was not the same as what occurs normally; hence, the metaphor only holds in a special case of slowing the heart. Another approach to understanding dynamics is to model them, where the model becomes the metaphor. When asked by Edmund Hailey how Newton knew the orbit of the moon was elliptical, Newton responded that he had derived it from creating a mathematical model. Note that the orbit of the moon was already known to be elliptical from descriptions by Johannes Kepler (1571–​1630) based on astronomical observations of Tycho Brahe (1546–​1601). Newton applied his newly invented calculus (invented simultaneously by Leibnitz) to derive the orbit of the moon. Newton first visualized the moon being trapped in a box (the metaphor for the orbit) to constrain the moon’s position over time. He then progressively increased the number of sides of the box and eventually the elliptical orbit resulted. Mathematical modeling of the neuronal action potential by A.  Hodgkins (1914–​ 1998) and A. Huxley (1917–​2012) predicted that four ionic conductance channels would be involved in the neuronal action potential, which was subsequently demonstrated. Early scientists were skeptical of mathematical metaphors for phenomena, despite Galileo’s statement that Philosophy [i.e., physics] is written in this grand book—​ I mean the universe—​which stands continually open to our gaze, but it cannot be understood unless one first learns to comprehend the language and interpret the characters in which it is written. It is written in the language of mathematics, and its characters are triangles, circles, and other geometrical figures, without which it is humanly impossible to understand a single word of it; without these, one is wandering around in a dark labyrinth. (Galilei, 1957, pp. 237–​238) Mathematical analyses have contributed greatly to the understanding of science in general and medical science in particular (Bochner, 1966). Certainly, there was and is a continued confidence that mathematical metaphors (analyses) would render any dynamical process tractable (see Wolfram, 2002, p. 1132). However, it was found that certain physical systems (such as long-​range weather) defied

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mathematical prediction and that certain explicit mathematical functions were not solvable.

Chaos and Complexity An example of a system that can be described explicitly by a mathematical metaphor proved unsolvable (at least explicitly in algebraic or integral functions) is the three-​body problem, such as the orbits of the earth, moon, and sun when all three are considered simultaneously. While the two-​body problem is solvable, such as the orbits of the earth and moon, even Newton recognized the difficulty and indeed potential intractability of the three-​body problem, commenting in 1684 that “to define these motions by exact laws admitting of easy calculation exceeds, if I am not mistaken, the force of any human mind” (from a draft of De Motu, tentatively dated in December 1684; Wilson 1989). Henri Poincaré (1854–​1912) was perhaps the first to demonstrate the solution of the three-​body problem as highly dependent on initial conditions and described the unpredictability of the results as being among the hallmarks of chaos and complexity. This means that starting from nearly the same points in space, the subsequent orbital motions can vary unpredictably. Clearly, the moon remains in orbit around the earth and sun and does not fly off; nevertheless, the solution of the three-​body problem can be only approximated. For chaotic systems, the mathematical descriptions are relatively simple, although highly nonlinear. For complex systems, just the number of interacting entities renders explicit solutions unpredictable. In these latter systems, the number of entities reaches a point where the system goes from predictable to unpredictable. There is a qualitative change with the increased quantities. This jump to complexity certainly renders the process metaphor, particularly reductionism, untenable. The behaviors of chaotic and complex systems are not random; they are deterministic—​determined by laws. Many do have regularities in their behaviors and provide mechanistic insights. For example, limit cycles demonstrate that certain chaotic or complex systems converge on a specific regularity. Perhaps one of the earliest and well-​described chaotic systems is the Lorenz attractor (Figure 14.3), from the work of E. Lorenz’s attempts to model atmospheric convection currents, such as occur in the air during weather events. He attempted to mathematically model the two-​dimensional flow through a fluid (such as air) based on variations in temperature, gravity, buoyancy, heat flow, and fluid resistance (http://​mathworld.wolfram.com/​LorenzAttractor.html). His model demonstrated dependency on initial conditions. Just slight changes in the values of the various factors used produced very different results, rendering the model unpredictable. However, the model was determinant, meaning that the calculations were specific and not random.

Dynamics

FIGURE 14.3  The

Lorenz attractor was first derived from a simple model of convection in the Earth’s atmosphere. From Stewart (2000).

The Lorenz attractor describes the changes over time of the models of three properties of the physical system. When the solutions for these properties were plotted over time, the result was the Lorenz attractor. As can be seen in Figure 14.3, even though the predictions of the model were unpredictable, the behavior of the model’s properties over time was not random but rather has a specific structure. The structure can be seen as trajectories of points in the three dimensions over time. Whereas it is impossible to predict ahead of time which trajectory occurs, all trajectories will be of similar shape and near each other in the property space. Similar concepts of attractors apply to complex systems as well. Chaos and complex systems provide a new metaphor by which to understand medicine (Higgins, 2002). Complex systems have a remarkable potential for self-​ organization, even though involving relatively simple dynamics. In some cases, the initially appearing chaotic or unpredictable behaviors of the individual entities become

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organized or structured. An excellent example was provided by Nicolas Perony in his TED talk (Perony, N. Puppies! Now that I’ve got your attention, complexity theory; https://​www.youtube.com/​watch?v=0Y8-​IzP01lw), shown in Figure 14.4. A group of puppies is given some milk. Initially, the puppies appear distributed randomly around the saucer of milk (Figure 14.4A). As each puppy pushes to the saucer to obtain the milk, the puppies (Figure  14.4B) begin to walk around the saucer of milk in a pinwheel fashion (Figure 14.4C). It is unlikely that the puppies discussed this behavior ahead of time or that it was their natural inclination to move as a pinwheel. Rather, their only motivation is to keep moving to the saucer so as to get milk. As can be seen, the saucer is shaped like a wheel (Figure 14.4D). Thus, the path of least resistance is to move to the right or left because moving forward comes up against the obstacle in the middle of the saucer. In this case, the puppies rotate as a pinwheel counterclockwise but just as easily could have rotated clockwise. The system is

FIGURE 14.4 

Self-​organization in puppies. (A) A group of puppies is given milk. Initially, the puppies appear distributed randomly around the saucer (A and B). As the puppies push forward to obtain the milk, they begin to move as a rotating pinwheel, in this case clockwise (C). The puppies only know to push forward toward the milk and do not communicate their intentions to each other. However, the saucer is more like a circular trough with a center without milk (D). From Perony, N. Puppies! Now that I’ve got your attention, complexity theory; https://​www.youtube.com/​ watch?v=0Y8-​IzP01lw.

Dynamics

said to bifurcate to either rotating counterclockwise or clockwise but not both or neither. A most remarkable example of self-​organization is seen with frozen human embryos. Often, these seven to eight cell embryos are frozen at −30°C to −150°C, where essentially all biochemical activity stops. Once thawed, these embryos result in a 35% successful pregnancy rate (Zheng et al., 2008). The analogy would be a car that is turned off and then, without anyone’s intervention, turns itself on and drives itself to the shopping mall. That a frozen embryo could subsequently develop (evolve) into a college professor is not due to predestination on the basis of the genes and chemical processes “put to sleep” with cryopreservation. Perhaps, given a bit of a nudge by increasing thermal energy during the process of thawing, the dynamics were set in motion, resulting in dynamical self-​organization into a human. At this point in time, most of the discussions regarding chaos and complexity are mathematical rather than medical. In part, this is because the mathematics of chaos and complexity are themselves difficult. It is likely that medical science increasingly will be able to describe or derive biological dynamics in differential equations and that their subsequent solutions will provide a new era in understanding medical knowledge. Such descriptions of medical and biological phenomena like chaotic and complex systems will be a way of at last reconstructing descriptors from fundamentals consequent to reductionism.

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Medical Science Versus Medical Technology

Isn’t it interesting that myasthenia gravis has been found (circa 1980) to be due to the patient’s own antibodies directed against the acetylcholine receptor on the muscle? MEDICAL SCIENTIST 2: It is interesting; I wonder whether Huntington’s disease is a genetically mediated autoimmune disorder? MEDICAL SCIENTIST 1:

This conversation (which actually happened at a Neurobiology of Disease Workshop sponsored by the Society for Neuroscience) is an example of a technology looking for a question. Posed as a syllogism, it appears to be of the form if myasthenia gravis is an autoimmune disorder and Huntington’s disease is myasthenia gravis, then Huntington’s disease is an autoimmune disorder—​which is obviously invalid. Medical scientist 2 likely did not pose the question as a syllogistic argument to prove a point but rather as a metaphor to generate a testable hypothesis. The metaphor was a conjecture not yet disproved. However, the considerable epistemic risk raises the question of why make such a suggestion? The experimental methods applied to the case of myasthenia gravis demonstrating an autoimmune basis could just as easily be applied to Huntington’s disease. Yet the availability of the technology, irrespective of the underlying logic or lack thereof, would seem an unsatisfactory justification. It would seem that all one has to do is thumb through a medical dictionary blindly to find a disease and then apply the technology. A proponent might say “you don’t know that Huntington’s disease is not an autoimmune disorder, and therefore criticism of a proposal to examine an autoimmune basis for Huntington’s disease is unfair and such research should be funded.” However, this would be an example of argumentum ad ignorantiam or arguing from ignorance; for example, I don’t know that you are an honest person, therefore you must be dishonest. Short of an inherent self-​contradiction such as a implies (b and not b), one cannot state that it is not possible for 178

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Huntington’s disease to be autoimmune. The argument a implies (b and not b) is invalid because (b and not b) is false regardless of the truth value of b. As a valid proposition (a implies . . .) and a true premise, a cannot result in a falsehood (b and not b), then either premise a is false or the proposition that a implies must be invalid. The fallacies just described represent a pernicious and epidemic problem in medical science—​ technology looking for a question. Such medical research would be highly inefficient, relying more on luck than reason and thus a major problem. Or is it? History suggests that many important therapeutics were exactly the result of technology looking for a question. Having enough technologists looking for a question may be more effective, if not efficient, than having scientists looking for a technology.

Medical Science, Medical Technology, or Both? The approach of having a technology and then looking for a question—​the technology-​then-​question paradigm—​is not the same as the scientific method, where the question should come first and then technology be assembled to test the hypothesized answer to the question. (This discussion should not be considered to denigrate either science or technology as both are critical and interdependent. Rather, this is to illuminate the epistemic issues.) If, however, the technology-​then-​question paradigm is more effective at new discoveries, particularly in improving health, one has to ask whether the $30 billion used by the National Institutes of Health in championing the scientific method is a good investment. Perhaps it is just academic hairsplitting to attempt a sharp differentiation between medical science and medical technology. Perhaps it is sufficient to use terms such as “technology/​science” or “techno-​science.” However, unlike other more basic science-​oriented institutes, the National Institute of Biomedical Imaging and Bioengineering was not established until 2000, and the National Center for Advancing Translational Sciences (NCATS) in 2011, whereas the original National Institute of Health (singular) started in 1930. Thus, institutes more technology oriented were relatively late newcomers, suggesting hostility from the basic scientists. At the time these new institutes and centers were being created, particularly the NCATS, there was great opposition by those who thought it would divert resources from basic science (presumably hypothesis-​ driven rather than technology-​driven research). Thus, there may be some “cash value” to differentiating medical science from technology, at least in terms of funding.

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Lessons from the Past In the late 1880s, Paul Ehrlich (1854–​1915) used a blue dye to make cells visible under a microscope. These dyes were taken up by living cells; hence the term vital stains. At some point, Ehrlich had the idea that these dyes taken up by living cells, like bacteria, might damage or destroy these cells. In 1886, Ehrlich, with Arthur Leppmann (1854–​1921), demonstrated that methylene blue was taken up by neurons, leading to attempts to purposefully lesion nerves to mediate the pain of neuralgias. In 1891, perhaps reasoning by the Fallacy of Pseudotransitivity—​ hence metaphor—​Ehrlich, with Paul Guttmann (1834–​1893), used methylene blue to treat patients with malaria because the methylene blue avidly stained the infectious agent causing malaria (Guttmann and Ehrlich, 1891). Methylene blue is still used to treat fungal infections. A systematic screening of other potential agents led to an arsenical compound used to treat syphilis. Interestingly, the use of methylene blue for infections was found to have an effect on mood and gave rise to antidepressants and antipsychotics (Schirmer et al., 2011). This approach of systematic screening (technology-​then-​question par­ adigm) was taken up by the Bayer chemical manufacturing company, hiring Gerhard Johannes Paul Domagk (1895–​1964) in 1927 to conduct systematic trials of various dyes that Bayer manufactured. Domagk derived the sulfonamide Prontosil from red dye, which became the forerunner of modern sulfa antibiotics. Subsequent work demonstrated that sulfonamides disrupted cellular metabolism, leading to the use of such compounds to treat leukemia. Direct lineage from these approaches resulted in azathioprine for antirejection drugs in transplant, allopurinol for gout, and acyclovir to treat certain viral infections. It does not stop there: continued research into sulfonamides led to diuretics to treat fluid accumulation in the body, such as congestive heart failure, and to treatments for hypertension based on initial observations of increased urination in patients being treated for infections (Le Fanu, 1999). Similarly, sulfonamides were found to increase the thyroid gland size in rodents, which led to the use of these agents to treat hyperthyroidism. Patients being treated for infections with sulfonamides were found to have lower blood glucose levels, leading to its use in treating diabetes mellitus. Another example recently acclaimed is the discovery of a new antibiotic that potentially avoids bacterial resistance. The antibiotic was derived from a fungus isolated from a soil sample. Since the discovery of penicillin, scientists have searched systematically for different fungi and then tested the products of the fungi against many forms of bacteria. The most recent discovery was held as a triumph because scientists could not grow the bacteria in regular culture medium; they just grew it from the soil (Ling et al., 2015). The question here is whether this is science or technology. If it is technology, a reprioritization of investments, such as the NCATS versus basic science, may be worth considering.

Medical Science Versus Medical Technology

It could be argued that much of the off-​label use of US Food and Drug Administration (FDA)-​approved drugs and devices represents technology (a drug or device in use for an FDA indication) looking for a question (a use not indicated by the FDA). Nearly 20% of medications (the number is higher in certain disciplines) are used off-​label (Radley et al., 2006). This does not account for drugs and devices used in the past in an off-​label manner that subsequently gained FDA approval.

A Special Case of Technology Looking for a Question There is a serious problem in modern medicine of a special case of technology looking for a question. This special case is the preference for “objective” tests as opposed to “subjective” assessments by clinicians, as evidenced by the many grant proposals and manuscripts submitted for publication valuing “objective” tests with no justification other than technical “objectivity.” Allopathic physicians look for underlying causes according to biological principles while discounting, to a degree, the phenomenology. Consequently, allopathic physicians would put greater stock in objective tests. One perhaps unintended consequence is discounting the value of the patient’s symptoms (what the patient reports subjectively). There is no reason, a priori, that the judgment of a clinician cannot be evaluated in the same manner as any “objective” test. The same issues of positive and negative predictive values can be applied. Furthermore, in medicine, the final decision is inevitably based on the social, moral, ethical, economic, and political consequences of a decision in one direction versus a decision in the opposite direction (Montgomery and Turkstra, 2003). It is very difficult to see that some “objective” test can make such a decision. A salient example is the use of magnetic resonance imaging (MRI) and computerized x-​ ray tomography (CT). There is no doubt that the use of MRI scanning has revolutionized the practice of neurology, neurosurgery, and other fields of medicine as well. However, there is little doubt that MRIs are overutilized (Armao et  al., 2012). While multiple factors contribute to overutilization (Hendee et al., 2010), at least one factor, in my experience, is the notion that an MRI is a sufficient diagnostic test that belies misunderstandings of positive and negative predictive values. For example, the false-​positive rate for imaging abnormalities in patients with isolated headaches is approximately 10%. This rate is nearly fivefold higher than the incidence of true positives with respect to a cause of the headache (Kernick and Williams, 2011). This means that using imaging as a screening tool for the diagnosis of causes of headaches is likely to misidentify five patients as having structural brain abnormalities for every single patient who relevant has an abnormality. This is particularly a problem when tests are ordered by nonexperts.

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Although widely varying, an MRI costs approximately $2,500 (USD). The cost for a neurological consultation by an expert is about $500. In my experience, the typical situation is for the nonexpert referring clinician to order the MRI first and then refer if the MRI scan fails to satisfy the nonexpert clinician. Often, the expert would not have ordered the MRI scan. In addition, the referring clinician receives much more information from the expert neurologist than is possible from an MRI or CT scan report. The expert’s opinion is a much greater return on the investment. In a study of the use of CT scans for patients with headaches, 76% of the scans were ordered by a primary care physician; of those patients, 80% were not referred to a specialist (You et al., 2011). Of the scans performed, only 2% had any finding that potentially could play a causative role. In the United Kingdom, the risk for a patient presenting to a primary care physician with headache of having a primary brain tumor is 0.09% (Kernick and Williams, 2011). Interestingly, in one review, only 2–​16% of patients with primary tumors presented with isolated headaches. The implication is that additional findings, presumably detectable by a sophisticated neurological examination, are more likely to detect primary tumors than just obtaining imaging.

Mathematics as a Technology An interesting parallel is seen in the mathematization of science, particularly physics, with medical science following not that far in the future. Arguably, most use of the mathematics in physics until Newton was descriptive. For example, using astronomical data provided by Tycho Brahe (1546–​ 1601), Johannes Kepler (1571–​1630) described the orbits of the planets as elliptical because an ellipse best fitted the data. The situation was different with Newton. When asked by Edmund Halley how Newton (1643–​1727) knew the moon’s orbit was elliptical, Newton replied that he had derived it. The following is Abraham de Moivre’s account: In 1684 Dr Halley came to visit him at Cambridge. After they had been some time together, the Dr asked him what he thought the curve would be that would be described by the planets supposing the force of attraction towards the sun to be reciprocal to the square of their distance from it. Sir Isaac replied immediately that it would be an ellipse. The Doctor, struck with joy and amazement, asked him how he knew it. Why, saith he, I have calculated it. Whereupon Dr Halley asked him for his calculation without any farther delay. Sir Isaac looked among his papers but could not find it, but he promised him to renew it and then to send it him (http://​www. mathpages.com/​home/​kmath658/​kmath658.htm).

Medical Science Versus Medical Technology

Since Newton, mathematics did more than describe, it explicated. The argument has been made strongly by Salomon Bochner (1899–​1982) in his book The Role of Mathematics in the Rise of Science (1966) that most of the mathematical ideas that informed science were first exercises in mathematics and, only subsequently, were expropriated by physics. While perhaps the mathematical ideas were not shopped to find some scientific question to answer, one might nevertheless consider mathematical ideas as “off-​label” use and analogous to technology rather than basic science.

Science for Its Own Sake, Technology for the Sake of Others One demarcation between science and technology is the end point. Technology produces something of a tangible value—​a product going beyond the knowl­ edge contained in the technology. Thus, technology can be evaluated in the goods it produces. Science, in contrast, is deemed a value unto itself—​science for the sake of science. In the current era, scientific progress is largely determined by the resources provided to scientists. Some prioritization is necessary. The US National Institutes of Health requires at least some passing reference to improving the health of citizens. While perhaps easier to evaluate the potential contributions of technology prospectively, it is more problematic for science. Normal science, in Kuhn’s terminology (1962), appears to progress in a continuous function, and thus the potential of a scientific project can be evaluated by how it interpolates between or extrapolates beyond accepted scientific findings. However, it is unlikely that the progress of important (revolutionary) science is continuous and therefore does not readily admit interpolation or extrapolation: in other words, research of little epistemic risk but correspondingly little to gain. Indeed, science for its own sake risks reinforcing normal science at the risk of inhibiting important (revolutionary) science. The goal of medical science should be to produce goods that will enhance health and reduce disease, not unlike medical technology. The question then becomes whether the means of supporting medical science can be separated from supporting medical technology and, if so, which to prioritize. The competition in a zero-​sum game was evident in the opposition to the establishment of the National Center for Advancing Translational Sciences discussed earlier.

Experimentalism and Science as Technology Understanding the history of modern science can prove illuminating to the question of medical science versus medical technology. This is particularly true

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following the “scientification” of medicine in the late 1800s and early 1900s (see Chapter 6). This follows on the “experimentization” of science in the 1600s with the institution of the Royal Society (of London), whose motto was Nullius in verba or “take nobody’s word for it.” The connection between “experimentization” and technology can be seen in the debate attendant on the formation of the Royal Society and centered about Robert Boyle (1627–​1691) and his experiments with the air pump and Thomas Hobbes. A  great deal of attention was focused on the development of more advanced air pumps rather than on the implications of whether a vacuum could exist. Boyle did not appear to express much thought or at least to argue very strongly on any position concerning the metaphysical and religious implications; instead, he appeared to be enamored with the technical issue of how to improve the air pump. Boyle wrote when the Torricellian Experiment is made, though it cannot, perhaps, be cogently prov’d, . . . that, in the upper Part of the Tube, deserted by the Quick-​Silver, there is a Vacuum in the strict Philosophical Sense of the Word; yet, . . . ‘twill to a heedful Peruser appear very hard for to shew, that there is not One in that Tube. Further the Space deserted by the Quick-​ silver at the top of the Pipe, . . . One may be Invited to doubt, Whether a Vacuum ought to be thought so formidable a Thing to Nature, as they imagine She does, and ought to, think It? (Boyle, 1999–​2000) Hobbes’s opposition was that Boyle was engaged in technology, not science. Hobbes did not think that experimentalism, particularly when more focused on technology, alone would provide scientific knowledge. He wrote “not every one that brings from beyond seas a new gin [machine], or other jaunty device, is therefore a philosopher” (Hobbes, 1839, p. 82). For most thinkers at the time, the significance of the air pump centered on the metaphysical and religious implications of a vacuum, the presence of which was thought inconsistent with Aristotelian metaphysics and thus out of favor with the scholastic natural philosophers and inconsistent with religion. Perhaps the focus on technology in the 1660s was a way of avoiding the theological and political dangers of the time, with the Restoration of Charles II in England and the ensuing battles between the clergy and the secular state. Boyle may have been unconcerned about the metaphysics of the vacuum based on a faith that science and theology will be united by “Right Reason.” Thus, the scientist need not concern herself with what the ultimate cause or prime mover of things is as this will be revealed in due time by theology. Importantly, the scientist is in a better position to eventually understand and explain the cause and prime mover than any theologian (Mulligan and Boyle, 1994, pp. 235–​257). Samuel Coleridge (1772–​1834), perhaps from a perspective similar to that of Hobbes, objected to those engaged in science calling themselves “natural philosophers.” William Whewell reported on the debate between Coleridge and

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others, particularly Whewell, at a meeting of the British Association for the Advancement of Science (BAAS), writing Formerly the “learned” embraced in their wide grasp of all the branches of the tree of knowledge, mathematicians and well as philologers, physical as well as antiquarian speculators. But these days are past. This difficulty was felt very oppressively by the members of the BAAS at Cambridge last summer. There was no general term by which these gentlemen could describe themselves with reference to their pursuits. “Philosophers” was felt too wide and lofty a term, and was very properly forbidden them by Mr. Coleridge, both in his capacity and philologer and metaphysician. “Savans” was rather assuming and besides too French; but some ingenious gentlemen [Whewell] proposed that, by analogy with “artist”, they might form “scientist”—​and added that there could be no scruple to this term since we already have such words as “economist” and “atheist”—​but this was not generally palatable. (Whewell, 1834) Whewell later wrote “We need very much a name to describe a cultivator of science in general. I should incline to call him a scientist. Thus, we might say, that as an artist is a musician, painter, or poet, a scientist is a mathematician, physicist, or naturalist” (Whewell, 1840). Modern science became synonymous with experimentalism, which centered increasingly on technology, and the pace of science was largely determined by the pace of technology development. The conflation of science and experimentalism also affected medical schools, as evidenced by the Flexner Report on the status of medical education in North America (Flexner, 1910). The recommendations included closing many medical schools, with those remaining to be affiliated with universities. Medical students were to be trained in a scientific manner by faculty who themselves were engaged in medical research, and, profoundly, the medical schools, now dominated by experimentalists, were to have control of clinical instruction in hospitals.

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Irreproducibility in Biomedical Science

STUDENT:

Do you think we will ever be able to cure this kind of

cancer? PROFESSOR:

Certainly, we have cured it 100 times in mice.

This conversation is reminiscent of Mark Twain’s statement “giving up smoking is the easiest thing in the world. I  know because I’ve done it thousands of times,” which means to say that it is not. Researchers have frequently cured all varieties of cancer in rodents, only failing to reproduce the successes in humans (a form of conceptual irreproducibility). If reductionist rationalist/​allopathic medicine is vigorous, then surely the cure of cancer in mice should be replicated in humans, but it is not. Clearly, there is something amiss in the reductionist rationalist/​allopathic approach to medicine. And it is not just curing cancer in mice. Irreproducibility strikes at the very confidence placed in the scientific basis of rationalist/​allopathic medicine.

The Magnitude of the Problem A series of articles have highlighted a serious problem in biomedical research, particularly in the area of preclinical animal-​based research, which is supposed to be the basis for future human treatments (Begley and Ellis, 2012; Prinz et al., 2011). It is estimated that nearly 20–​25% of findings are irreproducible. The reasonable implication is that these studies originally claimed to generate new knowledge; thus, the inability to replicate these findings suggests that the original knowledge claimed, at the very least, is unreliable. This means that more than 20% of the knowledge scientists claim is now suspect, at least as it relates to preclinical animal-​based research. These results were false positives, statistical type I errors, which falsely convey the impression of solid evidentiary support. 186

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This problem of irreproducibility is much greater. The “other side” of false positives or type I  errors are false negatives or type II errors, with the latter claiming knowledge that something did not exist or did not happen. Type II errors indeed may constitute a silent epidemic. Unfortunately, relatively simple methods to avoid type II errors, such as power analyses, are rarely performed in the literature. In a review of randomized controlled trials for the treatment of elbow and shoulder injuries, those studies demonstrating negative outcomes, on average, only had a power of 41% (Pike and Leith, 2009). This means that there was only a 41% probability of actually finding a difference if a difference truly existed. Unfortunately, because of bias against negative studies, it is unlikely that we will ever know the full extent of irreproducibility in biomedical science. Consequently, this text addresses both type I and type II errors. The “outbreak” of false positive or type I errors had an almost visceral impact of “hurt” pride in the scientific enterprise. Most of these studies survived professional peer review during the proposal and grant funding stage and, subsequently, in peer-​reviewed publication. These studies were supposed to be true based on accepted methods and standards of evidence. Regarding type II errors, the scientific methods was supposed to provide confidence that experimental results found not statistically significant truly were negative. Not only do type II errors waste resources, they discourage further investigations that perhaps could have been very important. Concerns about the reproducibility of preclinical animal-​based research are just the tip of the iceberg. Consider the number of drugs and devices that have been recalled by the US Food and Drug Administration (FDA) after FDA-​ approved studies were to have demonstrated their efficacy and safety. Note that it is not as though the FDA-​approved studies were equivocal—​the studies apparently demonstrated strong statistical evidence of efficacy and safety sufficient to warrant recommendations of approval by FDA expert advisory panels. Since 1960, more than 175 drugs have been withdrawn by the FDA and comparable agencies in other countries. Most commonly, these drugs were withdrawn because of adverse effects that did not appear to be significant until follow-​up postapproval surveillance. If failure to demonstrate efficacy comparable to the clinical trials were considered, the list would be considerably longer. With respect to medical devices, the FDA recalled 113 devices between 2005 and 2009 (Zuckerman et al., 2011). Again, most were for serious adverse effects, and, if failure of efficacy were considered, the list likely would be much longer. Certainly, fraudulent research is unlikely to be reproducible; however, fraud constitutes only a small percentage of irreproducible studies. Furthermore, these studies were passed as acceptable by the vaunted peer-​review process. If the degree of irreproducible studies being published reflects failure of the scientific infrastructure and practitioners, then something clearly is amiss.

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Scope of the Issue A narrow sense of irreproducibility is seen in the failure to reproduce when using the exact same experimental methods and materials, such as reagents and species of laboratory animals. The range of possible explanations is real but limited: for example, fraud, differences in skills, and some unappreciated bias. Studies in biological systems that are chaotic or complex may result in narrow irreproducibility because of the dependency on initial conditions and bifurcations to different metastable states (Chapter 14). In the latter situation, irreproducibility may have nothing to do with methods, materials, experimental design, or statistical analyses. A broader sense of irreproducibility is conceptual and goes beyond the specific circumstances of a specific experiment. For example, why have not successes in research involving rodents been translated to similar successes in humans? In other words, why is the knowledge gained not generalizable to humans (Benatar, 2007; Hutchinson and Kirk, 2011; Maas et al., 2010; Seok et al., 2013; Tator et al., 2012)? One cannot just dismiss broad irreproducibility as “species differences.” Why should society invest an enormous amount of funds to support research on different species, such as rodents, entirely independent of its relevance to humans? More importantly, attributing the failure to “species differences” raises serious, perhaps existential, challenges to the reductionist approach underlying rationalist/​allopathic medicine. Irreproducibility is made worse because its discovery and, hence, resolution are not given an opportunity. Self-​censorship by scientists reticent to publish “negative” studies may reflect the culture of the scientific enterprise. Certainly, there is a reticence to pursue research that may demonstrate the irreproducibility of previous knowledge claims as priority is given to the first discoverer. For example, who remembers the second person to discover America after Columbus. What researcher would want to be number 2? A larger issue is the lack of awareness or appreciation of the problem of irreproducibility. These issues are more political or sociological rather than conceptual, with the latter being the concern here. Biomedical research has been extraordinarily successful at the deconstruction of phenomena, as entailed in reductionism, particularly in the era of molecular biology. The success of deconstruction may overshadow any doubt about the reconstruction requirements of successful reductionist biology. Rather, there seems to be an unquestioning faith that successful reconstruction will occur, eventually. Indeed, Story Landis, former director of the National Institute of Neurological Disease and Stroke, together with Thomas R. Insel, former director at the National Institute of Mental Health, produced an editorial in Science cautioning scientists, writing Already it is clear that, for the study of behavior, genomics is not destiny. Indeed, if genomic sequence “determines” anything behaviorally, it determines diversity. It is important that we be wary about extrapolating

Irreproducibility in Biomedical Science

from model organisms to humans. We must also avoid using small statistical associations to make grand claims about human nature. Obviously, we have much to discover before understanding how genes influence behav­ ior—​a discovery process that will closely involve the brain. (Landis and Insel, 2008)

Information Loss and Irreproducibility Scientific knowledge is information, and information fundamentally is nonrandom changes in states. For example, a sensible sentence in English consists of a series of states, places that can be occupied by a symbol, including blanks. Reading the sentence is to progress over a series of states where the symbols under focus change. A  sentence in which the symbols are distributed randomly over the states would be nonsensical; it would not contain information. When viewed from this perspective, information is the converse of entropy (a sense of randomness). In any closed dynamical system, the entropy of the system can only increase. In closed dynamical systems, information can only be lost, following from the Second Law of Thermodynamics as Applied to Information. With any irreversible reaction (transitions) within any closed system, entropy increases and information decreases irreversibly. The critical question becomes whether any operations applied on information systems, such as experimentation or clinical reasoning, create irreversible transitions—​ irreversible loss of information—​thus making reconstruction (reproduction) impossible and resulting in irreproducibility. For example, a mean and standard deviation can be reduced (extracted) from a sample. However, armed only with the statistical mean and standard deviation it is impossible to reconstruct the sample—​one can make only an approximation whose accuracy is highly problematic (Chapter 8). Randomizing subjects (human or nonhuman) into experimental and control groups risks irreversible information loss, increasing the risk of irreproducibility. The resulting descriptive statistics, such as means and standard deviations, reflect not only the experimental manipulation but also the effects of confounds. From within the study, it is impossible to disentangle the experimental effects from those of the confounds. Consider a clinical trial of a stroke prevention medication whose outcome measure, incidence of stroke, is confounded by family history, diabetes mellitus, and hypertension. Successful randomization means that the prevalence of each of the confounds is equal between the experimental and the control group, with the presumption that these confounds will “balance” out in the result. However, the “balancing” is not a negation or cancellation of the effects of the confounds, but rather an “averaging” of the effects of the confound. The presence of a risk factor in some subjects in the experimental group may affect the outcome measure. However,

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the equal prevalence of the risk factor in the control group is thought to “cancel out” the effect of the risk factor in the comparison between the experimental and control groups. The “cancellation” is a consequence of constructing and comparing the central tendency, such as the mean, in each group—​creating the l’homme moyen, the average man. This notion enables clinical trials but also is a source of irreproducibility from the Fallacy of Four Terms (Chapter 5). But what is this “average person” represented in the central tendencies of each group? It is an aggregate “person” who does and does not have hypertension, diabetes mellitus, or a family history. The results of studies such as those just described will have limited generalizability to the individual subjects; in other words, the results are unlikely to reproduce the individual subject because the subject does not have the same admixture of the presence and absence of risk factors. The individual subject either has or does not have a family history, diabetes mellitus, or hypertension. The results cannot just be applied to any subsequent individual, and additional information external to the study must be applied (Montgomery and Turkstra, 2003) to individualize the experimental results. Note that the addition of information originating external to the study means that the study is no longer a closed system from the standpoint of information thermodynamics. Interestingly, the same concerns attend the methods used to obtain the external information. There is no “free lunch” in the sense of the Second Law of Thermodynamics; information gained in one sense is often associated with a loss of information in another sense. The ultimate balance depends on the consequences of the information gained compared to the information lost. Balancing the consequences is a value judgment and rarely a statistical or evidentiary decision. Consider the statement by Landis and Insel: One important lesson from neurogenetics is that genomic variations in regulatory regions can account not only for how much of a protein is made, or when it is expressed, but exactly where in the brain a protein is expressed. Because brain function is specified by precise regional circuits, even small differences in the location of the brain cells that produce a particular receptor or an enzyme can result in large differences in function. Importantly, the link between genomic sequence and behavior is the brain: We cannot hope to understand how genomic variation influences behavior without understanding how genomic variation influences neural circuitry. (Landis and Insel, 2008) It is likely that the genetic studies referred to by Landis and Insel analyzed genetic material extracted from the brain tissue independent of any knowledge of the neural circuits involved. Indeed, it is probable that the neural circuits were destroyed in the very process of extracting the genetic material—​perhaps a necessary but an irreversible loss of information. However, according to Landis and Insel, the tradeoff is not acceptable if it were to stop there. Researchers

Irreproducibility in Biomedical Science

publishing results on the genetic analysis should not make any claims as to relevance of the genetic knowledge to the understanding of behavior. However, this goes against the reductionist approach critical to rationalist/​allopathic medicine, and, perhaps for this reason, it is not common practice.

Areas to Explore The Duhem–​Quine thesis holds that in a logical argument (an experiment, for example) that produces a false (failed) prediction, some single or combination of premises and propositions must be false or invalid. One cannot determine which is the case from the failed consequence alone. There are a number of potential causes of irreproducibility, which include (1) intentional misleading (fraud); (2) lack of sufficient information in the original publication to allow replication; (3)  technical errors leading to false positives or type I  errors (finding a difference or new knowledge claim that does not truly exist) and false negatives or type II errors (not finding a difference or new knowledge claim that does truly exist); (4) error of logic or reasoning in the original experiments; (5) failure to appreciate the need to or value in demonstrating reproducibility; (6)  inherent irreproducibility, at least within the conceptual frameworks typically employed; and (7)  the knowledge claim is just plain wrong even if it is not understood why it is wrong. This chapter focuses on those causes of irreproducibility that are conceptual in nature and have direct implications for medical reasoning. Consider translational research and animal models, where “translational” is exemplified by the National Center for Advancing Translational Sciences (NCATS) of the National Institutes of Health (NIH). The mission of NCATS, established in 2012, “is to catalyze the generation of innovative methods and technologies that will enhance the development, testing, and implementation of diagnostics and therapeutics across a wide range of human diseases and conditions” (http://​www.ncats.nih.gov/​about/​mission.html). The NCATS is consistent with the larger NIH mission “to seek fundamental knowl­edge about the nature and behavior of living systems and the application of that knowledge to enhance health, lengthen life, and reduce the burdens of illness and disability” (http://​nih.gov/​about/​mission.htm). Particularly of interest is the T0 program of the Clinical and Translational Research Award program, which is preclinical research aimed at defining the mechanisms of targets for disease intervention or diagnosis and lead molecules that could point to new therapies or diagnostics. Despite the promise of a research agenda that goes from a fundamental understanding of life, in reductionist terms, to mechanisms of disease identified in nonhumans to human relief, there is room for a healthy and reasonable degree of skepticism from historical analysis (Chapter 15).

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Epistemic risk is implicated in the failure of animal models to predict the corresponding human disease. Epistemic risk, particularly as it relates to metaphor or using the Fallacy of Pseudotransitivity to create animal experiments intended to reflect on humans, is composed of epistemic distance and epistemic degrees of freedom (Chapter 11). These include species differences in metabolism, physiology, and complexity. For example, species behaviors may be very different and likely relate to differences in the complexities of their nervous systems. The critical question becomes whether the difference between the rodent and the human brain is different in quantity or quality. Perhaps the only difference is in the number of neurons. If it were possible to increase the number of neurons in the rodent brain to equal those of the human brain, perhaps the behaviors of the rodent’s brain would approximate those of the human. The knowledge from the rodent neuron and thus brain operations could be extrapolated linearly to those of humans (Herculano-​Houzel,  2009). Assume that there is a qualitative difference in the behaviors of humans compared to their closest animal counterparts in behavior. If there is no difference other than linear scaling, for example, in the number of neurons, there must be a breakpoint where the behavior of the neurons makes qualitative changes beyond any quantitative changes. This discontinuity occurs despite the presupposition that neurons in less complex species are intrinsically no different from more complex species. This phenomenon is seen in complex systems and is discussed in Chapter 14. Perhaps a failure to recognize this qualitative discontinuity limits the ability to extrapolate from other animals to humans. For example, consider the work done by Shonesy and colleagues (2014). Their research involved creating a knockout mouse for the DAGLa gene that encodes diacylglycerol lipase α, resulting in a loss of 2-​arachidonoylglycerol (2-​AG), and then, as a check, knockouts following treatment with JZL-​184 to increase 2-​AG were studied. Measures to assess anxiety included the light–​ dark condition where mice could move between dark and lighted areas. Measures included the distance moved and the percentage spent in the light versus in the dark. These measures demonstrated a difference in the knockout mice but not in the JZL-​184-​treated knockout mice. Another measure was the NIH assay in which both male and female knockout mice were statistically significantly different, but the JZL-​184-​ treated knockout mice were not. There are a number of concerns related to this research. Foremost is the assumption that the behavioral tests employed were specific for anxiety. Note that the term “anxiety” was specific. But with respect to the rodent, the application of the term “anxiety” was an anthropomorphism used rhetorically to presume a relevance to humans. Equating the rodent movements between dark and light conditions with human anxiety creates epistemic risk, the Fallacy of Four Terms, and the subsequent risk for conceptual irreproducibility.

Irreproducibility in Biomedical Science

The study by Shonesy and colleagues (2014) also demonstrated that not all of the behavioral measures produced the predicted result. The problem is which to believe. To select one result over any other result risks confirmation bias. Logically, based on the Duhem–​Quine thesis, there is nothing within the experiments allowing us to favor one outcome over any other. To do so requires the import of knowledge external to the study. Indeed, Shonesy and co-​workers (2014) do so by creating a strawman argument, writing Importantly, we also found that pharmacological elevation of 2-​AG levels in DAGLa −/​− mice reversed anxiety behavior in the L-​D box and anhedonia in the SPT, but it was unable to reverse anxiety in the NIH assay. Because the NIH assay is extremely sensitive to CB1 signaling deficiency (Gamble-​George et al., 2013), it is possible that more-​complete 2-​AG restoration or longer duration of restoration would be required to see reversal in this test. From the perspective of this study, it also is likely that the study authors’ hypoth­esis is wrong, although that is not mentioned. The significant problem is that none of these concerns was discussed in any meaningful way, and the title itself, “Genetic Disruption of 2-​ Arachidonoylglycerol Synthesis Reveals a Key Role for Endocannabinoid Signaling in Anxiety Modulation,” appears as a foregone conclusion that endocannabinoids are relevant in anxiety. Also note the loaded word “key” applied to the presumed role of endocannabinoids. Assuming for a moment that there are other factors, what is it in the experiment that makes the endocannabinoids “key” compared to any other factor? From the report, it would seem entirely reasonable to invest considerable resources to pursue analogous human studies. Yet there is a very high probability that such human studies would fail, further contributing to the list of false-​positive, type I preclinical studies. Another area contributing to irreproducibility involves a logical fallacy called the Fallacy of Confirming the Consequence (Chapter  5). This fallacy is of the form if a implies b is true and b is found true, then a must be true. In this case, a is the hypothesis, b is the set of observations, and the implication (implies) is a causal relation between a and b; hence, where b becomes the testable prediction. The fallacy results because b could be true regardless of the truth or falsehood of a. Subsequent studies may demonstrate that c actually caused b, and the prior experiments claiming a as the cause would be considered irreproducible. It just may be that some claims of knowledge are inherently irreproducible, at least within the conceptual frameworks typically employed. Few would argue that for objects moving very slowly relative to the speed of light, such as planets of the solar system, Newton’s laws of motion and gravitation could predict the motions of the planets accurately. While it is true for the orbit of

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the moon about the earth, it is not true of the motion of the moon, earth, and sun—​called the three-​body problem (Chapter 14). There is no exact analytic solution to allow a prediction of the three planets. Rather, there are methods that can model the motions approximately. Does this mean that the laws of physics are not reproducible?

Reconstruction: The Other Half of Reductionism and Relevance to Reproducibility Reproducibility in translational and clinical research typically involves reproducibility in the broad sense, such as relating from one (presumably simpler) level to another level (presumably more complex), such as genes to cells to tissues to organs to systems to organisms; from animal or computational models ultimately to humans; or from the samples to the populations of concern. Many of these can be conceptualized as a reconstruction from the experiments completed to future experiments or clinical experiences. However, each invites irreproducibility because of the Fallacy of Four Terms, shown here in Argument 16.1. Note that the Fallacy of Four Terms does not mean that such studies are destined to be irreproducible. Argument 16.1 Major premise: Condition x (in lower specifies) responds to intervention y. Minor premise: Higher species have condition x. Conclusion: Higher species should respond to intervention y. The critical issue becomes whether condition x in lower species represents condition x in higher species. If condition x in lower species is very different from condition x in higher species, then the syllogism given previously becomes the Fallacy of Four Terms and would have a high epistemic risk. Thus, there would be a high risk of irreproducibility when intervention y is attempted in the higher species even though intervention y was successful in the lower species. In medical research, a high epistemic risk is not necessarily an absolute impediment to conducting research. There may be a very high value in treating condition x with little cost or risk to intervention y other than the costs of the research. If this is the case, the relatively higher risk of an irreproducible result may be justified. Nevertheless, qualitatively assessing the epistemic risk and comparing estimates of cost and benefit may provide a useful approach to planning biomedical research in both higher and lower species.

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Medical Solipsism

The patient had a deep brain stimulation (DBS) system to suppress involuntary movements. The patient presented to the emergency room with severe involuntary movements, and the battery of the DBS system was found to be exhausted. The patient was seen just 2 weeks prior, and, had the treating physician checked the battery, the treating physician would have realized the battery was low and a replacement would have prevented recurrence of the severe involuntary movements. The patient would not have needed to come to the emergency room and then be admitted to the hospital. PHYSICIAN 2: Well, I have patients with electrical stimulators, and I don’t check the battery. PHYSICIAN 3: I don’t either. PHYSICIAN 4: Then it is our policy that physicians do not have to check the battery. PHYSICIAN 1:

Physician 1 believes that all clinicians treating patients with implanted electrical devices should check the battery of the device as a matter of standard practice. By whatever means medicine uses to establish standard care, clinicians failing to check a battery are a concern. The other physicians took their own lack of concern as the standard of care even though battery-​checking takes minor effort and could have prevented suffering. The latter physicians’ position would seem unreasonable, but how could they consider it legitimate?

Knowledge in Medicine Perhaps this issue is not one of medical knowledge but rather of ethics. After all, the issues in this case do not involve questions of physics, chemistry, or biology. If medical knowledge is only what science is, the problem is not one

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of medical reasoning. But, even then, what constitutes scientific knowledge is highly problematic and of continuing debate since the time of Bacon (1561–​ 1626) and Descartes (1596–​1650). Science certainly is not just exact and true knowledge of transcendental (not contingent) objective realities, perhaps as Descartes would argue. Baconian science does not fall fully formed from formalized experiments. As to the truth, rightfulness, falseness, or failure in the question of whether the battery should be checked, this is not an issue that typically requires experimentation. One aspect of knowledge relates to its consequences. Knowledge incorporates information defined as nonrandom state changes (Chapter  16). Thus, knowledge can be seen in nonrandom actions whether by scientists or clinicians. Indeed, in 1833, William Whewell coined the term “scientist” as one who “does science.” Thus, whatever science is, it is somehow related to what scientists do (Whewell, 1834). If scientists act in some nonrandom manner, there must be some information that the scientists use, no matter what that information is, whether it be driven by Kant’s synthetic a priori or categorical imperative (1781), some regard for the external laws, some politically imposed behavior (Kuhn, 1962), or some route to sociological or psychological status (Osbeck et al., 2011). Evidence of knowledge can be seen in nonrandom acts, implying that knowledge need not be only explicit or declarative. In such cases, skill or procedural fluency, such as backing up a tractor-​trailer, implies knowledge even if that knowledge cannot be explicitly declaratively described. Just as science knowledge is reflected in what scientist do, medicine knowl­ edge can be reflected in the nonrandom acts of clinicians. In the case presented at the beginning of this chapter, the physicians acted in a nonrandom manner to establish that a physician does not have to check the battery of an implanted electronic device as a standard of practice. This nonrandom act reveals a specific notion of the nature of knowledge. If the actions center on ethics, then the nonrandomness suggests a knowledge of ethics. One could dispense with the question by casting it as an ethical question whose analysis requires deconstruction into beneficence, respect for autonomy, nonmaleficence, and justice—​ the core ethical principles. However, specific cases may result in clashes between the principles, for example, between beneficence and respect for autonomy. A resolution of conflicting principles requires adjudication by appeals to the moral theories, such as deontological duty, egalitarianism, utilitarianism, and libertarianism. But these principles and theories often are incommensurable, without the same base currency for comparisons and decisions. The resolution of the case described at the beginning of the chapter appears not to have been resolved by principles of moral theories, which would have required the physician to check the battery because doing so entails minimal cost and high value. The ethical status of the decision not to check the battery was

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established a posteriori and out of habit rather than knowledge. Subsequently, the committee of physicians, with the opportunity of deliberation and forethought, chose as policy that any clinician need not check the battery of an implanted electrical device. Like the physician who failed to check the battery, the committee did not act on principle of knowledge, yet their actions are taken as legitimate and affecting the actions of future clinicians, thus a type of implicit knowledge. The only justification is “that is what the committee decided,” a form of solipsism. Lawsuits over malpractice often turn on what is the standard of care. Historically, the standard of care is what similarly situated physicians would do. In other words, the ethical standard is descriptive but then taken as normative. Descriptive is what is done, and normative is what should be done. In the aforementioned case, what is descriptive—​he historical actions of physicians—​ is not taken as normative—​what should be done and what need not be done. Increasingly, courts have recognized that such descriptive standards can be self-​ serving and protective of interested parties who promulgate the standards, arguably as in the case described here. Many courts are turning to the standard of what a “reasonable” similarly situated physician would do. These courts now introduce a normative standard by interjecting “reasonable” physician. The notion is that “reasonable” implies an appeal to knowledge and principle. Assuming that the standards of care constitute a medical truth or knowl­ edge claim, failure to practice at the standard of care constitutes a falsehood or invalid knowledge claim. Further assume that the community of physicians constitutes a single organism with a collective knowledge; then, failure throughout the community constitutes a false or invalid knowledge claim. When viewed from this perspective, the descriptive ethic becomes knowledge claim A  is true or valid because the organism believes it to be valid; no other justification or validation is necessary. Furthermore, there is no truth or valid knowledge that exists outside of what the organism believes to exist. This position is an example of solipsism. There may well be situations where truth or validity is only what is the consensus of the empowered constituency, yet medical science and decision-​making would not seem to be among them, contrary to the postmodernists.

Nature of Solipsism Solipsism is an epistemic position positing that all that is knowable is contained in one’s thoughts (the “one” reflects the individual as well as a collective of individuals when collective assertions or actions are necessary). As an example of solipsism, F. H. Bradley (1893) wrote “I cannot transcend experience, and experience must be my experience. From this it follows that nothing beyond my self exists; for what is experience is its states.” The solipsism appears

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unwarranted unless one considers its denial. “I cannot transcend experience” becomes “I can transcend experience,” a very problematic notion from a Baconian science perspective. “Experience must be my experience” becomes “Experience is somebody else’s experience,” but, if that is the case, how does the individual come to know about somebody else’s experience without it becoming the experience of the individual? Thus, the denial of the solipsistic claim would result in a contradiction that “one can know the experience of others without having the experience.” The absurdity of the denial of solipsism is taken as support for the solipsistic claim (proof by reductio ad absurdum). Solipsism as an ontological notion is trivial in the philosophical sense. One can only know what one knows in his or her mind/​brain. Evidence of this can be seen in attempting to assert the contrary; that is, someone may know something that is not in his or her mind/​brain. Solipsism in the ontological sense can admit new knowledge (or the forgetting of knowledge) provided it is experienced by the individual or collective. Epistemic solipsism holds that there is no source or object of knowledge external to what is conceived already. By what means new knowledge is acquired is highly problematic. It cannot be that elements of the external world press upon the mind/​brain to create new knowledge, as according to John Locke (1632–​1704). The mind/​brain is not a tabula rosa, an empty slate upon which experience writes. This is seen in the limits of induction, the A  Priori Problem of Induction, and abduction (Chapter  5). Descartes (1596–​ 1650) demonstrated that any appeal to external reality as an arbitrator of knowledge fails. One can doubt all things but the fact that one can doubt (an evidence of thinking). Thus, the only knowledge is the self-​knowledge that one exists to think. Descartes resorted to the benevolence of God not fooling his mortal charges, thus vouchsafing human knowledge (arguing deus ex machina). One could argue that there are certain self-​evident truths that require no justification and recommend themselves as starting materials for an entire system of knowledge claims. These would be analogous to Kant’s analytic a priori. Indeed, the logical positivists, circa 1920s, attempted to achieve this but failed. One could default to fiat or consensus to determine what knowledge claims are. However, such approaches immediately risk doubt because justification does not arise from the knowledge claims themselves but from the power of those to enforce them. One could argue that true or valid knowledge claims reflect the common knowledge of all relevant persons and, in a utilitarian sense, reflect some sort of truth or justification. However, there are significant political risks to allowing such a position. It is difficult to argue that the solipsist’s ontological claim that knowledge of A is just A if one is first forced to accept the solipsistic epistemic position. The problem is conflating the ontological and epistemic positions of solipsism. In medicine, the debate on the ontological claims of the solipsist could

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be based on the ethical, moral, economic, political, sociological, psychological, and political consequences. Perhaps the greatest concern the solipsist physician and healthcare professional can have is rendering themselves immune from the ethical, moral, economic, political, sociological, psychological, and political consequences of their actions. If society is going to enter a debate with the community of clinicians about the standards of care, what is going to be the basis of that debate? As has been discussed throughout this book, medical decisions are seldom based on absolute certainty. The solipsist needs only the tiniest chink in the armor of certainty to exploit radical skepticism. If a clinician is shielded from any of these consequences, the debate is over before it begins. The clinicians are given free rein, at times with disastrous consequences.

The Use of Consequence to Resolve the Solipsist’s Advantage What profession is more immune to the consequence of their actions than the medical profession, particularly in view of the importance of physician actions? After medical school graduation, medical licensing, and possible board certification, which is not necessary (in the United States, only 75% of physicians are board certified; Young et al., 2010), there is little recurrent or formal review of clinician decision-​making. Consider medical school and postgraduate medical education, where new physicians practice medicine under the supervision of an experienced physician. The question arises about how successful is such supervision in ensuring that new physicians learn to practice reasonable medicine? There are a number of grounds on which to be concerned. First, experienced physicians are products of the same learning process as new physicians. The same learning process that has led to the large numbers of medical errors raises concerns about the experienced physician’s qualifications to instill reasonable practice in the new physician. Second, the more formalized structure of postgraduate medical education would appear capable of ferreting out medical errors and thus educate to prevent. One formal means for doing so is the mortality and morbidity conferences that are supposed to be universal in medical institutions. These conferences are designed to ensure a review of cases with questionable outcomes; in fact, in a majority of legal jurisdictions, these provide immunity from legal discovery in order to facilitate frank and open discussions. Yet studies demonstrate that few actually engage in discussions of errors (Gore, 2006; Orlander and Fincke, 2003; Pierluissi et al., 2003). Third, the workloads of residents have become highly compressed. Most residency programs are supported financially based on the number of hospitalized patients (“beds”) the resident service covers; generally, the same

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or fewer than when I was a resident. However, the average length of stay has been reduced drastically. This means a very rapid turnover of patients relative to the number of beds. Thus, the workload of residents has increased dramatically (Goitein et  al., 2013), perhaps leaving less time for actual deliberative education. Compounding the problem has been the rigid restriction of hours a resident can work, which itself is a reasonable change. This restriction of hours also comes when a greater amount of time is mandated to didactic teaching, which renders education at the bedside very limited. Yet it is likely at the bedside where the best learning of reasonable medicine occurs. This is where every medical decision has to weigh the social, ethical, moral, political, economic, and biological consequences in the context of the patient’s unique circumstance. For example, questions of ethics require specification, which is consideration of the unique circumstances and cannot be determined in the abstract (Beaumont and Childress, 2013). It is questionable whether the didactic lecture format (as opposed to case-​or problem-​based learning) is effective. Surely the threat of medical malpractice should be sufficient to enforce clinicians’ concern for errors of medical reasoning. However, most often this is not true. First, the threat of medical malpractice may be an empty one. When asked whether concerns of being sued for malpractice might change a physician’s behavior, the chairman of a medical department in a respected medical school said “our patients do not sue.” Second, the patient or the patient’s surrogate must recognize the failure of reasonable medical practice—​most often a distinct disadvantage. There was a movement arguing for clinicians and healthcare delivery systems to admit mistakes; however, defensive legal advice easily drowned out such attempts. Rather, patients or patient surrogates sue primarily over bad outcomes, which is perhaps why neurosurgeons and orthopedic physicians have some of the highest malpractice insurance rates. The consequence is that the potential of a myriad of lesser errors of medical reasoning go undetected and uncorrected. Furthermore, the offending physician is unlikely to receive criticism from colleagues (a practice actually instilled in the American Medical Association’s Code of Ethics of 1847). Third, most medical malpractice suits are settled out of court, with results kept confidential. A national database records settlements paid to plaintiffs on behalf of the defendant; however, its effectiveness in correcting errors of medical reasoning is uncertain.

The Solipsism of Evidence-​Based Medicine Evidence-​based medicine was introduced in the 1990s to establish greater rigor in medical decision-​making. As conceptualized initially, evidence-​based medicine considered different categories of evidence that were not necessarily hierarchical. These categories included (1) randomized clinical trials, (2) case series,

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(3)  case reports, (4)  expert consensus, and (5)  expert opinion. Subsequently, evidence-​based medicine has become synonymous with randomized controlled trials, thus discounting case series, case reports, expert consensus, and expert opinion, often to the point where these alternative forms of evidence are perceived as illegitimate. If these alternatives are illegitimate, what is to guide medical decision-​ making in the absence of randomized controlled trials (RCTs)—​likely the majority of medical decision-​making? Consider the use of the dopamine agonists ropinirole and pramipexole for the treatment of Parkinson’s disease. Both were found to be effective in early Parkinson’s disease and were associated with lower risks of the long-​term complication of involuntary movements (dyskinesia). However, there was no RCT that compared ropinirole directly to pramipexole. So how is a physician to choose between ropinirole and pramipexole? When a group of movement disorder neurologists were asked on what they based their preference on, none offered any specific rationale. When it was suggested that a comparison could be made between ropinirole and pramipexole based on their efficacy and adverse effects relative to levodopa, all of them said this was not appropriate. Note that the design, execution, and analysis of the randomized clinical trials relating the effects of each agonist to levodopa were virtually identical. These highly respected movement disorder neurologists would only accept an RCT that directly compared ropinirole to pramipexole, clearly shutting off future discussion much as solipsism would do. As to which dopamine agonist to choose, the default position appears to be a flip of the coin. One could calculate the ratio of efficacy of each dopamine agonist to carbidopa/​levodopa along with the ratio of adverse effects. The ratios could be compared where a/​b > c/​d is an empiric fact resulting from the studies; where, for example, a is the incidence of hallucinations on agonist A, b is the incidence of hallucinations on carbidopa/​levodopa in the study of A, c is the incidence of hallucinations on agonist C, and d is the incidence of hallucinations on carbidopa/​levodopa in the study of C. If every relevant measure of the effects of carbidopa/​levodopa in the pramipexole study (b) equals the effects of carbidopa/​levodopa (d), then b = d and the relation become a/​b > c/​b. If one multiples both sides of the inequality by b, the expression becomes a > c; it could not be otherwise. This would be reasonable grounds to prefer agonist A over agonist C. The only counter would be to deny that b = d, which is the effect of carbidopa/​levodopa in the two studies, but this is an empirical matter to be settled by data, not an a priori rejection of the comparison. In the case of hallucinations, the risk was greater with pramipexole than it was with ropinirole (Etminan et al., 2003). Are the pernicious effects of the solipsism of evidence-​based medicine in the absence of RCT nihilism or adventurism? Some physicians may offer no treatments not supported by RCTs or take a laissez-​faire approach in which clinicians think they may do as they please. Neither approach is reasonable.

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The Solipsism of US Food and Drug Administration Approval The effects of evidence-​based medicine synonymous with RCT are mirrored in the effects of US Food and Drug Administration (FDA) approval of drugs and devices for certain disease indications. Considering FDA approval as the only legitimate source of knowledge means that those issues on which the FDA has no opinion are problematic, much like the situation just described in the absence of randomized clinical trials. The nihilistic version holds that any use of a drug or device for an indication on which the FDA is silent constitutes experimental or investigational status. Frequent misuse of the terms “experimental” or “investigational” are often used to deny treatments. Holding the FDA as the sole arbiter of medical knowledge would be an exercise in solipsism—​only what the FDA thinks is true is true. This nihilism reflects an epistemic bias, as noted by Radley, who wrote “Despite sufficient evidence justifying some off-​ label practices, lack of FDA approval means that off-​label uses are not given the same degree of scientific scrutiny as labeled indications” (Radley et  al., 2006). The FDA (2014) recognizes that they are not the sole arbiter of appropriate medical care, writing If physicians use a product for an indication not in the approved labeling [off-​label use], they have the responsibility to be well informed about the product, to base its use on firm scientific rationale and on sound medical evidence, and to maintain records of the product’s use and effects. Use of a marketed product in this manner when the intent is the “practice of medicine” does not require the submission of an Investigational New Drug Application (IND), Investigational Device Exemption (IDE) or review by an Institutional Review Board (IRB). However, the institution at which the product will be used may, under its own authority, require IRB review or other institutional oversight. Legally, the definitions of “experimental” and “investigational” are statutory and are found in the Code of Federal Regulations (section 312.3[(b]) for “experimental” and section 50.3[(c]) for “investigational”), and the use of these terms rests entirely on research rather than medical practice, not whether the drug or device is approved by the FDA for off-​label use. Courts ruled that physicians need not inform patients regarding the off-​label use of an FDA-​ approved drug or device and that “off-​label” connotes no medical information, which would not be the case if the off-​label use truly was “experimental” or “investigational.” In Klein v.  Biscup (109 Ohio App.  3d 855 [Ohio Ct. App., [1996]), the court held that [t]‌he decision whether or not to use a drug for an off-​label purpose is a matter of medical judgment, not of regulatory approval. By analogy, the off-​label use of a medical device is also a matter of medical judgment,

Medical Solipsism

and as such, subjects a physician to professional liability for exercising professional medical judgment. Off-​label use of a medical device is not a material risk inherently involved in a proposed therapy which a physician should have disclosed to a patient prior to the therapy. (cited in Riley and Basilius, 2007) Admittedly, physicians may be held accountable for their off-​label use should it fail to conform to the standards of care. Riley and Basilius (2007) go on to suggest that “whenever a physician prescribes a drug [device] for off-​label use, it should be based on (1) the doctor’s own expert medical judgment; (2) peer-​ reviewed articles reflecting sound scientific evidence; (3) documented medical practice; (4)  if possible, the opinions of the physician’s local colleagues; and (5) a desire to directly benefit the patient for whom it is prescribed.”

The Solipsism of Balkanized Medicine In the United States, healthcare delivery has the appearance, if not fact, of being divided up into separate and, if not warring, at least noncooperating states. Increasingly, health insurers restrict patient options to obtain care out of the closed network of clinicians tied contractually to a specific insurer. Indeed, for many patients, the additional costs of going “out of network” can be astronomical. While the situation is not as bad for medical emergencies that make it medically necessary to obtain out-​of-​network care, the financial consequences can be enormous and, in fact, prohibitive. If the consequences of going out of network are prohibitive, the situation is in effect saying that the only legitimate medical care or knowledge is that which is obtainable within the network. The effect clearly is solipsistic. The patient is confronted with a declaration by the insurer that only the insurer knows what reasonable care is for the patient. What happens when the care needed is not provided by the insurer? In one survey, 32% of patients who went out of network did so voluntarily because there was no in-​network physician available (Kyanko et  al., 2013). Various governmental agencies are critically assessing whether narrow networks are providing sufficient access to essential medical services as required by the Patient Protection and Affordable Care Act of 2010, although recent actions by the US government may weaken this protection.

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Critique of Practical and Clinical Medical Reasoning

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In 1781, Immanuel Kant wrote his Critique of Pure Reason, followed by his Critique of Practical Reason, in 1788. The first was a masterpiece of analytical philosophy dealing with ontology and epistemology. The second masterpiece analyzes how one can function in a real world, particularly when confronted with the greatest threat—​other persons. Kant’s Critique of Practical Reason still strongly influences modern ethics and morals. The Critique of Pure Reason was a criticism of pure theoretical (scientific) reasoning, while the Critique of Practical Reason was an antidote to the Critique of Pure Reason, suggesting some suspicion of pure theoretical (scientific) reasoning. Suspicion of scientific or science-​directed medical reasoning suggests an alternative with the distinction of being clinical or practical reasoning. Are clinical and scientific reasoning and science distinct and separate, each to be considered as different disciplines with unique ontologies and epistemologies? Considerable ethnographic and historical evidence suggests distinctions, but are these distinctions ones of principle (ontological), that is, inevitably and always distinct, or in practice, that is, unresolvable because of inadequate means to demonstrate their commonalities or distinctions (epistemological)? If one of practice, can there ever be a time when the practical limitations can be overcome and thus practice is established as being or not being synonymous with science? If not, clinical and scientific reasoning and science are distinct whether in principle or in practice, at some level, and the clinician will never become only a scientist. If so, teaching better clinicians necessarily will involve something that science, alone, cannot provide. An affirmative does not reduce the importance of science and scientific thinking. While science and scientific thinking are necessary, they are not sufficient conditions for sound medical decision-​making. In many ways, the prior chapters are analogous to Kant’s Critique of Pure Reason in that they define the epistemic limits and vagaries of medical logic

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and the science. The previous chapters are a “critique of pure medical reason.” Medical logic will be advantaged greatly by clear and comprehensive true premises and valid propositions. Furthermore, valid propositions are advantaged by sound epistemic approaches, such as those that underlie statistics and probability theory. However, it would be a mistake to think that the clear premises of biomedical science and the valid propositions provided by statistics and probability theory alone are sufficient. For that reason, at least a chapter analogous to Kant’s Critique of Practical Reason is needed—​a “critique of practical medical reason.”

Distinctions Without Distinctions? First, definitions of terms are necessary to avoid begging (presupposing) the distinctions. Thus, “practical” or “clinical” cannot be defined merely as the converse or denial of science or what is scientific, although this often is done. Being scientific means methods held as direct, empirically rigorous, proven to certainty (incontrovertible), objective, and causally reductive. Science is a set of known things about the universe, with a certainty derived from applying the scientific method. Knowledge in science is of universal claims and general principles as it would be impossible to have knowledge of every particular constituent of the universe. Thus, the epistemic challenge is how to relate the vast number and variety of constituents in the universe to some smaller set of universal claims and general principles (Chapter 7). These problems confronting the scientist are not different in kind from those confronting clinicians. Practical reasoning often is characterized as tentative, fluid, and admissible of multiple interpretations, but, importantly, it is often indirect and not necessarily causally reductive. As such, practical reasoning does not lend itself to establishing incontrovertible universals and general principles. Thus, it would seem that practical reasoning and its consequent clinical claims is of a different kind than scientific reasoning and its consequent science. Practical reasoning in the context of caring for a particular human becomes clinical reasoning. By extension, clinical reasoning and clinical claims become different in kind compared to scientific reasoning and science claims. Clearly, practitioners of practical and clinical reasoning utilize scientific reasoning and science. In this sense, scientific reasoning and science become domains or subsets within the larger set of practical and clinical reasoning. How large or small is that subset relative to the set of practical and clinical reasoning? Furthermore, how is it that the clinician uses science and scientific reasoning? Is it a like a tool, a hammer, that the clinician takes up to solve a problem, or is it actually part of the clinician? Are science and scientific reasoning like the clinician’s hand that, being used as a hammer, is confused as a separate hammer?

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There is a sense that advances in biomedical science will demonstrate that any system predicated on anything but biomedical science will just fade away as some unfortunate past confusion. Practical and clinical reasoning will become coextensive with science and scientific reasoning, leaving little room for anything else. Science and scientific reasoning are not tools but part and parcel of the clinician, not just the hand but the whole body. This notion was seen in the American Medical Association’s Code of Ethics in 1847 claiming the mantle of science in the name of the rationalist/​allopathic physician and in contrast to the empirics (Chapter 6). This strong notion of science and the scientific in clinical reasoning is seen in the response by Thomas Insel, Director of the National Institute of Mental Health, to the publication of the Diagnostic and Statistical Manual of Mental Disorders (DSM-​5; http://​www.nimh.nih.gov/​ about/​director/​2013/​transforming-​diagnosis.shtml, see also Chapter 1). Science will explicate many extra-​scientific concerns as just illusions or unnecessary, the philosophical notion of eliminative materialism (Churchland, 1979). While eliminative materialism may be operational in science, such an approach will not be epistemically unproblematic for medical reasoning. If eliminative materialism, in the form of ontological reductionism, depends on biomedical science, then eliminative materialism will have a hard go in medical practice. Recall Groopman’s description in Chapter  2 of a brilliant pediatric cardiologist who was being too logical (scientific), resulting in a near catastrophe (Groopman, 2007, pp. 142–​149). Groopman would appear to argue that the set of practical and clinical reasoning is far larger than the subset containing science and scientific thinking. Clinical intuition often is offered as that aspect of clinical reasoning not contained within the subset that is science and scientific and will be addressed later. Another source for the apparently distinct nature of practical and clinical reasoning lying outside the subset of science and scientific thinking is seen in ethnographic studies of practicing clinicians. These studies suggest that the clinician’s modus operandi more resembles an intuitive or inductive method rather than the hypothetico-​deductive approach characteristic of the scientific method. Ethnographic studies are descriptive, but should they be normative. Indeed, a fashion in medical education utilizing case-​based learning suggested imitating and reinforcing the reasoning methods in experienced clinicians. However, it is not clear that this is the most optimal approach to teach the naïve. Studies of the development of expertise from medical students to clinicians support very different stages (Schmidt and Rikers, 2007). Just assuming that the naïve are the same as experienced clinicians invites the Fallacy of Four Terms (Chapter 5). A movement seeks to change medical education and training to a solely case-​ based approach, reasoning that students need only to imitate their mentors. This approach suffers from the Actor–​Action Fallacy—​just watching an accomplished actor recite Shakespeare does not demonstrate that the actor

Critique of Practical and Clinical Medical Reasoning

actually understands the meaning of what he is saying. This is not to say that the actor does not understand; instead, there is no way of knowing by just observing the actor acting. Similarly, just because the experienced clinician does not provide a declarative narrative does not mean there is no underlying rationale—​logical, scientific, or otherwise. It may be a matter of the complexity of the decision-​making. Relatively easy decisions may arrive rapidly (perhaps precipitously), giving the impression of sound reasoning on first impressions (Woolley and Kostopoulou, 2013). It may be only in complex cases that a declarative narrative of decision-​making based on scientific reasoning becomes evident (Patel et al., 1990). Interestingly, the current vogue of evidence-​based medicine, now synonymous with randomized clinical trials, implies that reductionist and causal biomedical science is unnecessary. If injections of a ground-​up kitchen sink demonstrated statistically significant improvement in disease A, then there are no grounds in evidence-​based medicine not to advocate the use of a ground-​ up kitchen sink, even if there was no plausible notion by which a ground-​ up kitchen sink improves disease A (Turner, 2017). Lest one think that the preceding argument is hyperbole, note that medicine has used ground-​up tree bark and flowers—​predecessors of aspirin, quinine, and digoxin—​long before the underlying science was known. Evidence-​based medicine gains its certainty from the power of statistical inference, and if statistics are thought to be scientific, then evidence-​based medicine is scientific and perhaps science. However, evidence-​based medicine need not invoke other characteristics of scientific reasoning and science, in which case accepting evidence-​based medicine as science would do violence to what is thought to be science and scientific. Yet the centrality of evidence-​based medicine argues that there is more than science and the scientific in clinical reasoning. In North America, this dichotomy between practical and scientific reasoning is seen in the Flexner Report of the Carnegie Foundation, which dramatically changed medical education (Chapter 7). Critics (including Dr. William Osler, the paragon of modern medicine) opposed the injection of scientism into the teaching of medicine. The consequence was a dichotomization of medical education into the preclinical (scientific) and clinical (practical) years, although this model is fading toward case-​and problem-​based learning. Nonetheless, the Flexner Report reinforced the appearance of dichotomy between practical and scientific reasoning in medicine. Even in medical schools maintaining a relatively separate basic science curriculum in the first two years, many medical students see this as merely paying dues until they reach the clinical services where the real learning of medicine takes place. Another source of distinction between practical, clinical, and scientific reasoning and science derives from a notion of science in which the latter derives from induction from particulars to universals. Clinical reasoning may be seen

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as reasoning from universals and general principles to the particulars of a specific patient. As discussed later, this may be a misconception of clinical reasoning, just as seeing science as extrapolating from particulars to universals and general principles. For example, if one accepts mathematical modeling, such as that done by Isaac Newton as science (Bochner, 1966), Newton and every mathematical and computational modeler are conducting science by reasoning from generals or universals—​mathematics—​to particulars, such as the moon and earth (Chapter 15). In some ways, Cartesian science represents reasoning from universals (principles) to particulars (Henry, 2004, pp. 73–​114). Is the dichotomy between practical and scientific reasoning more apparent than real? Perhaps those holding a sharp distinction view “practical” reasoning to be less scientific than it actually is and view “scientific” reasoning to be more scientific that it actually is. Accepting for the sake of argument that pattern recognition (intuitive or inductive) is a form of practical diagnosis, it cannot operate if reductive causality (science) is abandoned. What particular observations are taken to be relevant while others are not (the A Priori Problem of Induction; see Chapter  5)? This decision necessarily is antecedent to the selection of relevant phenomena and the exclusion of irrelevant phenomena before pattern recognition can lead to a practical diagnosis. To say that the clinician has prior art does not answer the question as the same question can be made of those prior clinicians who established the art. Furthermore, assuming that no two patients with the same diagnosis are exactly alike, how can one have confidence in pattern recognition? There is considerable historical evidence of science being less “scientific,” as pointed out by modern critics (Thomas Kuhn, The Structure of Scientific Revolutions, 1962; Paul Feyerabend, Against Method: Outline of an Anarchist Theory of Knowledge, 1975). Furthermore, demonstration that the scientific method relies on the Fallacy of Confirming the Consequence and the Fallacy of Pseudotransitivity is evidence of being less than “scientific.” Extrapolating to medical reasoning, the most common method is the hypothetico-​deductive approach, that is, the scientific method, even in those holding medical reasoning as inductive (Norman et al., 1999). Given this, then at least these types of clinical reasonings may have more in common with science and scientific reasoning than not. Modern allopathic medicine is consistent with one aspect of science: proceeding from particulars to induce the universal principles and then deriving particulars. As Aristotle wrote, “the physician does not prescribe what is healthy for a single eye, but for all eyes, or for a determinate species of eyes” (Posterior Analytics, quoted by Zaner, 1998). Furthermore, None of the arts [techne] theorize about individual cases. Medicine, for instance, does not theorize about what will help to cure Socrates or Callias, but only about what will help to cure any or all of a given class of patients,

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this alone is its business. Individual cases are so infinitely varied that no systemic knowledge of them is possible. (Rhetorics, quoted in Zaner, 1998) The diagnosis attributed to an individual patient is derived from a class of patients who present with a specific constellation of findings. The diagnosis of one particular individual is not different from the diagnosis of a different particular individual from the same class. Otherwise, how would a clinician know that the treatment of one person with diagnosis A will respond, even if other patients with diagnosis A responded? Although not the only approach to medicine, nevertheless, diagnosis in the allopathic sense is generalization from the particulars (a certain patient and the patient’s certain findings) to a universal (for that class) just in the same manner as the reviewer held for science. The commonality of practical, clinical, and scientific reasoning and science becomes clear when one asks “What is the most basic epistemic act?” The diagnostician (prognosis and treatment decisions are a form of diagnosis) is a “finder of facts,” with the facts being diagnoses (conclusions) based on observations, such as symptoms, signs (physical findings), and laboratory testing. But, at a fundamental level, how is this different from a scientist or a judge, who are also finders of facts? The scientist has the scientific method and quantitative analyses, and the judge has courtroom procedures and rules of evidence. The logician has axioms and rules in the form of truth tables. Certainly, the subject matter and the rules of inference may differ but, fundamentally, the challenge is the same, which is to have certainty in the facts found.

Clinical Intuition Practical, clinical, and scientific reasoning and science, as discussed previously, involve variations in deduction (such as the Fallacy of Confirming the Consequence) and induction. Another aspect attributed to practical and clinical reasoning is intuition, held to be different from induction. The following discussion centers on the role of intuition in clinical reasoning. However, the discussion is very circumscribed, as any attempt at a full discussion is far beyond the scope and limits of this text. Indeed, a PubMed search yielded nearly 900 citations on the search term “clinical intuition.” The interested reader is referred to Held and Osbeck (2014). Clinicians’ intuitions are made rapidly, such as on first impressions. Such a rapid assessment is presumed too fast for a deliberative process, such as would involve science or scientific reasoning. But this may be an invalid assumption. For example, consider Figure 18.1 containing the figure of a dog. Often, it takes a considerable search over time to find the dog. Next, view Figure 18.2 upside down, and the dog is outlined. Now view Figure 18.1 again, and the dog is immediately recognizable.

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FIGURE 18.1  There

is a dog somewhere in this abstract figure. The challenge is to find it.

The knowledge of the dog’s location from viewing Figure 18.2 allows an almost immediate recognition when Figure 18.1 is viewed again, which is not the case when Figure 18.1 was first viewed. Clearly, viewing Figure 18.2 upside down provided knowledge that could be used in a meaningful way when viewing Figure 18.1 the second time. Also, the knowledge is brought to bear in an instantaneous recognition relative to human perception. Evidence that knowledge is brought to bear is reflected when examining Figure 18.3, where again a time-​consuming search is required to find the dog. In this case, the knowledge obtained from viewing Figure 18.2 upside down is not helpful when viewing Figure 18.3. The implication is that just the speed of coming to a clinical decision does not mean that no rationale, akin to science and scientific reasoning, is operational. Another method used to demarcate instances of clinical intuition from science or scientific reasoning is asking whether the clinician recognized any kind of logic or rationale. Where this is not possible, it is inferred to be intuitions devoid of logic or rationale. Also, clinical decisions in the absence of what was

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FIGURE 18.2  The

image is the same as in Figure 18.1; however, the dog is outlined in blue and is

upside down.

thought necessary and sufficient information have been considered evidence of intuition. A study by Woolley and Kostopoulou (2013) identified such cases among a group of family physicians and used the Critical Decision Theory to explore the mechanisms of those intuitions. One type of intuition is “gut feeling,” an example described by Woolley and Kostopoulou (2013): “During further information gathering, an intuition experienced as a feeling cast doubt over the initial interpretation. In all gut feeling cases reported, the intuition signaled alarm, often in response to a single cue that ‘did not seem right’ or an unexpected pattern of cues.” However, note that there was a prior decision that did not comport with the expected pattern of cues. The expected pattern of cues constituted prior knowledge, much like viewing the dog in Figure 18.1 after viewing Figure 18.2 upside down. Consequently, the “gut feeling” of discomfort presupposes a knowledge and a means, or logic, to relate the presupposed knowledge to the current observations. Another type of intuition is recognition, described by Woolley and Kostopoulou (2013): “In recognition cases, a diagnosis is formulated quickly and with little information. Although recognition cases appear similar to first

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FIGURE 18.3 

Somewhere in this abstract figure is a dog. Having seen the dog before in Figure 18.2 and for a second time viewing Figure 18.1, it still takes a time-​consuming search to find the dog in this image, although perhaps less time than in Figure 18.1.

impressions, physicians were aware of conflicting information and/​or the absence of key symptoms and signs.” Insight is another form and is described as occurring when “no pattern of cues is recognizable initially, and no satisfactory interpretation is formed, although several diagnoses are considered, and the physician engages in extensive information gathering. A clear interpretation is suddenly and rapidly formed that integrates and explains all the symptoms and signs, sometimes as the direct result of a single piece of information from long-​ term memory suddenly coming into the physician’s awareness.” In each of these types of intuitions, the clinician experiences a conflict, and it is that conflict that is the intuition. The source of conflict was an initial decision that is intuited as being unlikely, out of the ordinary, or difficult to verbalize; possibly meaning that the clinician was unable to explain in a declarative manner that would follow some rationale. But the initial decisions held as improbable are based on prior experience and just illustrate an unrecognized Bayesian inference based on a probability calculus. Clearly, there are strains of scientific reasoning in such Bayesian inference. Thus, these types of intuition are implicitly probability theories and specifically Bayesian inference and scientific.

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The third source of experienced conflict underlying clinical intuitions occurs when the initial intuition appears too complex and difficult to verbalize. The verbalization likely relates to the ease of creating a causal narrative and skepticism regarding excessive complexity (Law of Parsimony or Occam’s Razor). The evolution of expertise, perhaps culminating in clinical intuition, also suggests that practical, clinical, and scientific reasoning and science are differences in degree rather than kind. Schmidt and Rikers (2007) argue that junior medical students “rapidly develop mental structures that can be described as rich, elaborate causal networks that explain the causes and consequences of disease in terms of general underlying biological or pathophysiological processes. When confronted with a clinical case in this stage of development, students focus on isolated signs and symptoms and attempt to relate each of these to the pathophysiological concepts they have learned.” When asked to explain their clinical decision-​making, junior medical students are likely to include more case details (suggesting an inability to winnow the details) but use more references to basic science than would more experienced clinicians. According to Schmidt and Rikers (2007), the next stage is encapsulating pathophysiological knowledge, where a lower level (basic science) is subsumed into higher level concepts such as clinical syndromes. The final step is a conceptual movement from an encapsulation to “illness scripts,” a type of narrative. Interestingly, the narrative is more of a procedure or protocol for arriving at a clinical decision. Becoming more procedural, the “illness scripts” become harder to articulate and, consequently, would be defined as intuitive. Such illness scripts have lost their tether to the basic science learned as a medical student. Having lost their tether, it is not difficult to see how clinical decision-​ making based on illness scripts would be seen as nonscience or nonscientific reasoning. Interestingly, reintroduction of science and scientific reasoning may be helpful in difficult clinical cases (Patel et al., 1990). Strikingly, Woolley and Kostopoulou (2013) reported a survey where family physicians were asked to report two cases, one in which the application of intuition resulted in a good outcome and one where it did not. Of a total of 24 cases, only 7 had poor outcomes. This suggests a confirmation bias toward identifying or recalling cases of good outcomes, which could provide a false sense of validity of such intuition.

Fundamental Limits of Science and Scientific Reasoning Necessitating Practical Reasoning If there is a fundamental incompleteness in science, then something other than science is necessary to complete the understanding. Incompleteness, in the sense used here, holds that there are claims taken as true that are not

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provable in science by any scientific reasoning, the latter in a deductive fashion where the claim necessarily follows from what is given (taken as true) in science. An example is the Heisenberg Uncertainly Principle, which holds that two commentary properties, such as location in space and momentum, cannot be simultaneously known to an arbitrary degree of exactness—​an example of physical-​theoretic incompleteness. Consider science as a formal logical system based on fundamentally proved universals and general principles that form the basis of all phenomena. Science has not achieved such a complete understanding, but this does not necessarily mean that it could never be complete. There is faith that a complete understanding of human biology may be possible, leaving little room or necessity for practical or clinical reasoning. However, other formal systems robust or complex enough, such as mathematics and computer science, have been found to be incomplete. Thus, it is likely that science similarly will be found fundamentally incomplete (Stephan Hawking, “Gödel and the End of Physics”; (http://​www. damtp.cam.ac.uk/​events/​strings02/​dirac/​hawking/​). Analogous to science as a formal system, mathematics is also subject to incompleteness with number-​theoretic incompleteness. Gödel’s incompleteness theorem holds that there are statements, generally regarded as true, that have not and cannot be derivable (a form of proof) (see Hofstadter, 1979). For example, the mathematical statement “2 + 2 = 4” is provable but the statement “2 + 2 = 4 is valid” cannot be proved. Another example is the reasonable conjecture that “every even integer greater than 2 can be expressed as the sum of two primes” (Goldbach’s conjecture), which seems to be true and has held for a great many instances; but it has not been proved, as yet, universally true. Perhaps the Goldbach’s conjecture is an example of mathematical intuition. Computer programs, as a formal (logical) system, are incomplete. In addressing the halting problem, Alan Turing demonstrated that no computer program can determine whether any other computer program will reach a true result in a finite time. This means that there are computer programs that will solve important problems, but it cannot be determined ahead of time that those programs will succeed (Turing, 1937, 1938). Indeed, physical-​theoretic and number-​theoretic incompleteness and the halting problem are variations on information-​theoretic incompleteness (see Calude and Stay, 2005). Perhaps one demonstration of information-​theoretic incompleteness is complexity and chaos. A key feature of complexity and chaos is the difficulty of predicting the precise outcome of any dynamic physical system with sufficient complexity, and surely the human counts as such (Chapter  14). Yet, despite the unpredictability of human behavior and hence disease, this unpredictability does not mean that the outcomes are not given by science or understandable by scientific reasoning. By extrapolation, just because clinical decisions are arrived at by intuition does not mean that science and scientific reasoning are not relevant or instructive.

Critique of Practical and Clinical Medical Reasoning

As discussed in Chapter  13, science and scientific progress depend critically on hypotheses, which fundamentally are examples of the Fallacy of Pseudotransitivity. In these cases, the hypothesis is derived by its similarity to what may already be known and its potential contributions related to the degree of dissimilarity. As discussed in Chapter 13, the similarity between the muscle weakness induced by curare and myasthenia gravis led Dr.  Mary Walker to hypothesize that anticholinesterase agents that reversed curare-​induced weakness may help improve the weakness causes by myasthenia gravis, which was subsequently demonstrated to be the case. Note that it was not simply the presence of weakness, as causes of weakness other than myasthenia gravis would not have responded to anticholinesterase agents. Why did Mary Walker’s metaphor contain weakness associated with myasthenia gravis as the target domain? Perhaps it was other aspects of weakness common to curare and myasthenia gravis that are not associated with other forms of weakness, such as stroke. Perhaps it was intuition. More generally, what makes one target domain more “attractive?” Often, esthetic qualities are invoked, such as eloquence and symmetry (Stewart, 2007). Occam’s Razor (the Law of Parsimony) is often invoked in scientific reasoning, where the simpler solution is the better solution—​but only to a degree. As Einstein was purported to have said, “Everything should be made as simple as possible, but not simpler.” Similar aesthetic notions are invoked in clinical reasoning; for example, the admonition “when you hear hoofbeats, think of horses, not zebras.”

Clinical Meaningfulness Clinical meaningfulness may demarcate practical and clinical reasoning from scientific reasoning and science from practical and clinical reasoning. Often, clinical meaningfulness turns on nearly every aspect of the human condition—​ for example, the value of health and life. These issues would seem beyond science or scientific reasoning (evidence to the contrary is presented in Chapter 1). Randomized control trials only speak to what is statistically significant and not to what is clinically meaningful. Translation from statistically significant to clinically meaningful requires the importation of knowledge or consensus independent of the randomized control trial and is the role of practical and clinical medical reasoning. Clinical meaningfulness is a complex notion, but its basic purpose—​ the care of individual patients—​is reflective. Thus, it ultimately is an ethical issue informed by moral theory. For example, clinical meaningfulness can be considered in terms of consequence in a utilitarian perspective, but this, historically, always has been difficult because of competing issues among stakeholders and shareholders. Whose interests gain dominant or maximal utility, and thus

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govern the consequences? While randomized clinical trials demonstrated statistically significant differences from placebo in favor of an active agent, can practical reasoning justify the use of a prohibitively expensive treatment? Can the results of such a randomized clinical trial be clinically meaningful? But what if the patient has a right to the treatment (there is a deontological commitment to the patient)? If so, then should a lottery for treatment be held in order to prevent societal financial bankruptcy? Who would find this acceptable? Perhaps one might quantify clinical meaningfulness and then prioritize the treatment decisions. Quality of life adjusted years (QALY) quantify assessments of a treatment in terms of quality of life and the duration of life extended (Hirth et al., 2000; Torrance and Feeny, 1989). One then could divide the QALY by the costs of accepted treatments that then become a benchmark for clinical meaningfulness. But note that this approach entails scientific induction (gen­ eral principles induced from accepted treatments), much like the development of ethical principles described in Chapter 1. Clinical meaningfulness could come from the notion of “numbers needed to treat” (Andrade, 2015). For example, consider a treatment demonstrated to reduce the risk of stroke by 0.1%. This means that 1,000 patients need to be treated to reduce the number of strokes by 1. Clearly, this is less clinically meaningful compared to a risk reduction by 10%, which means that only 10 patients need to be treated to prevent a single stroke. But it is important to note that there is nothing within the trial establishing the number to treat to prevent as ethically acceptable. Again, an inductive approach as described previously may be possible. Clinical meaningfulness can become a method of prioritization, as attempted in the Oregon Plan. In the plan, an ordered list was created based on utilitarian criteria. If a particular patient’s medical condition was relatively low on the list, the patient would not have his or her treatment covered by Oregon Medicaid (Perry and Hotze, 2011). The process was subsequently abandoned because a waiver from the federal government could not be obtained because there were concerns of constitutionality based on the Americans with Disabilities Act of 1990. The point here is not to provide an extensive discussion of ethics as there is insufficient space to do it justice. There are a number of excellent books on the subject (Beauchamp and Childress, 2013; Jonsen, 1998). The purpose is to demonstrate that considerations of ethics and morality figure in clinical decision-​ making, and it appears irresolvable to a reductionist and mechanical account, again lending distinction between practical and clinical reasoning. However, an inductive scientific approach is at least possible, as described earlier, blurring the distinction between practical, clinical, and scientific reasoning and science. Additional considerations when attempting to formulate clinical meaningfulness include translating findings from randomized control trials to the treatment of an individual patient, thus risking solipsism (Chapter  17). For

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example, randomization of subjects to counterbalance potential confounds and then using summary statistics reflects only on the l’homme moyen, the average man (Chapter  8), which has no counterpart in reality. The clinician is challenged to interpret the findings from the nonexistent subject amalgam, the l’homme moyen, to the individual patient. There is no logic or calculus within the randomized control trial to assist. Rather, the clinician must use her judgment about how the comorbidities of the individual patient relate to the nonexistent subject amalgam. But again, this is not to suggest that such reasoning cannot be scientific.

Clinical Assessment Recommending a specific treatment presupposes an operating diagnosis. However, diagnosing is just as fraught as is utilizing randomized control trials of treatments. Diagnosis involves collection and analysis of data just as any scientific study does, regardless of whether data are reports by patients in response to questions asked, findings on the physical examination, or results of laboratory and imaging studies. At work are the A Priori Problem for Induction, the issue of variation versus diversity in the variety of data, and the consequence of the Fallacy of Pseudotransitivity, all leading to the risk of the Fallacy of Four Terms. Each are influenced by the metaphysical (ontological and epistemic) presuppositions of the particular medical program. For empiric practitioners, the patient’s history is critical because there is no underlying set of economic principles by which to reconstruct the patient’s disease. Rather, each patient’s condition was seen as idiosyncratic, and the process was to best align the patient’s symptoms and signs to a particular therapeutic agent based on “like treats like” (see Chapter  6). For the empirics, history-​taking was necessarily extensive and exhaustive. With the advent of histopathology and clinical–​pathological correlations, rationalist/​allopathic physicians relied less on the patient’s history, believing surer data lie in the physical examination and then later in laboratory and imaging tests. Indeed, laboratory and imaging tests today largely define the diagnosis irrespective of what the patient’s history and physical examination might indicate. Yet laboratory and imaging tests have some rate of false-​negative and false-​positive results. Furthermore, the value of these tests rests on the prior probabilities that translate specificity and sensitivity to positive and negative predictive values. Although unappreciated, the patient’s history and physical examination affect the positive and negative predictive values by changing the prior probabilities confronted by the laboratory and imaging tests, as was discussed in Chapter  15 related to computerized tomography and magnetic resonance imaging scans for headache.

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History-​taking is fundamentally problematic. Human conversations suffer from the inscrutability of reference that results in the indeterminacy of translation (Quine, 1960). A classic example is the patient complaint of “being dizzy,” which could mean vertiginous, lightheaded, faint, unsteady, shaky, wobbly, giddy, and more. Practical medical reasoning first recognizes the inscrutability of reference—​what does dizzy mean?—​in making a translation of the patient’s complaint into medical data sufficient to assist in diagnosis. The wise clinician takes steps to reduce the indeterminacy and then looks for other shared experiences to form a point of reference. The clinician asks “remember when you were a child and you would spin around fast and then you would stop. Is the feeling of dizziness now like that feeling when you stopped spinning?” The clinician then may go on to ask “did you ever have a funny feeling when you jumped up from a chair too fast? Is your feeling of dizziness like that?” Complaints of numbness or tingling can be referenced to the experience of “hitting one’s funny bone,” a leg “going to sleep,” or the sensation produced when a dentist “numbs” the mouth for a procedure. Each of these exercises fundamentally address the risk for the Fallacy of Four Terms. In this case, the complaint of “dizziness” is the bridging term that links the diagnosis (universal major term) to the particular patient (the minor term) in a syllogism. The patient is not a disinterested party to the conversation with the physician or healthcare professional. This was described beautifully by John Donne in his aphorism: I observe the Phisician, with the same diligence, as hee the disease; I see hee fears, and I feare with him, I overrun him in his feare, and I go the faster, because he makes his path slow; I feare the more, because he disguises his fear, and I see it with more sharpnesse, because hee would not have me see it. (Donne, 1624) The wise clinician recognizes that easing the patient’s fear and anxiety leads to better data. In fact, such is the duty of the clinician. Practical medical reasoning recognizes the importance of not asking leading questions whose very structure would bias the patient’s report. But such clinical reasoning is a recognition of the necessary use of the Fallacy of Confirming the Consequence in the hypothetico-​deductive mode of medical reasoning that risks Confirmation Bias (Chapter  5). Similar concerns attend the physical examination. As rationalist/​allopathic medicine, in its scientism faith, becomes increasingly synonymous with laboratory tests and imaging, physical examinations risk becoming perfunctory, and, consequently, their value to improving the positive and negative predictive values is lost. Many specific signs sought during the physical examination are difficult to interpret because of the inverse problem, but this does not negate the value of the history and physical examination. Indeed, laboratory tests and imaging likewise are at risk of the inverse problem. An inverse problem exists whenever

Critique of Practical and Clinical Medical Reasoning

there are multiple causes of the same phenomenon. It is not possible to infer which of the multiple causes is responsible for the phenomenon. For example, in disorders of movement, the muscle activities mediate the observable phenomenon. Yet the muscle represents the final common pathway. Many sources influence muscle activities, including (1)  muscle, (2)  neuromuscular junction, (3) peripheral nerves, (4) lower motor neurons in the brainstem and spinal cord, and (5)  upper motor neurons in the brain. One cannot discern from muscle weakness which structure is responsible. Other signs must be looked for that differentiate among the muscle, neuromuscular junction, peripheral nerve, lower motor neuron, and upper motor neuron. The other signs shape the prior probabilities.

The Diagnostic/​Therapeutic Scheme Any medical decision based on some assessment of data hinges on the positive and negative predictive values (see Chapter  5). What is the probability that the clinician will identify the disease or the best treatment (positive predictive value)? What is the probability that the clinician will identify someone as normal or that a treatment will not be effective or needed (negative predictive value)? The positive and negative predictive values depend not only on the specificity (the ratio of true negatives to the number of true and false negatives) or the sensitivity (the ratio of true positives to the number of true and false positives), but also on the prior probability. Definitive information regarding prior probabilities often is absent. Yet the proper practice of medicine requires some estimate of prior probabilities. Estimates can come from analogies to other situations. The ensuing risk of the Fallacy of Four Terms and epistemic risk requires judgment as to the degree to which the analogy will hold. Most medical decisions are not single, independent considerations but rather the process of a sequence of considerations where each step in the sequence influences the prior probabilities in the subsequent step. For example, when testing for syphilis, a venereal disease research laboratory (VDRL) test is obtained initially; however, the test has low specificity, and, because of the relatively low prior probabilities, the number of false positives is too high. Should a patient’s VDRL test be positive, a fluorescent treponemal antibody (FTA) test is obtained. The key is that prior probabilities for a person having syphilis in those having a positive VDRL test are increased sufficiently so that the risk of a false-​positive FTA test is acceptably low. It is important to distinguish that the sequence of obtaining first a VDRL test and then an FTA test is not the same as testing for an autoimmune cause of a peripheral neuropathy in a patient diagnosed with diabetes mellitus, as discussed in Chapter  6. The prior probabilities of diabetes mellitus and an

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autoimmune disorder being associated with a peripheral neuropathy are independent, and thus the probabilities associated with the positive and negative predictive values are independent of the other. A negative VDRL test reduces the probabilities of a positive FTA test. Diagnosing diabetes mellitus in a patient with a peripheral neuropathy does not lessen the probability of the patient having an autoimmune disorder. Thus, if it is reasonable to test for an autoimmune cause in the absence of diabetes, then it is just as reasonable to test for an autoimmune disorder in the presence of diabetes mellitus.

Difference Between What Can Be Done and What Should Be Done The decision to make a specific diagnosis or to offer a specific treatment includes but goes beyond the positive and negative predictive values and thus beyond the strictly scientific and statistical. The larger concerns are the consequences of failing to make a positive diagnosis or making a treatment recommendation versus making a false-​negative diagnosis or failing to treat. The consequences have to be taken in their widest context, including social, political, ethical, moral, economic, and psychological in addition to what might be considered strictly medical. Furthermore, the consequences must be parsed among shareholders (those with influence in the outcome) and stakeholders (those affected). For example, while doable, it is not clear when a woman under the age of 50 without significant risk factors should have a mammogram. However, obtaining a serum vitamin B12 level in a patient with dementia may seem a waste of time and money because of the rarity of the dementia secondary to a vitamin B12 deficiency, yet most shudder at the possibility of missing a potentially reversible cause of dementia. Similarly, table salt is iodinated and milk is vitamin D fortified without questioning whether any particular patient is at risk for goiter or rickets. What a clinician should do is the purview of ethics, while why is the purview of morality. An analysis of practical medical reasoning, particularly the ethical dimensions, benefits from specification or instantiation. As pointed out by Beauchamp and Childress (2013), it is difficult to discuss principles of ethics in the abstract. In analyzing any ethical situation, the exact circumstances of the situation are as important as the ethical principles involved. Thus, the premises and propositions involved in practical medical decisions derive as much from the situation as they do from the principles. Thus, it could be argued, for example, a restrictive formulary of available treatments is just as important as the pharmacology or mechanisms of action of the treatment. Society generally has not been of one mind regarding the underlying moral status of healthcare. Which should prevail:  egalitarianism, deontological (or Kantian) duty, libertarianism, or utilitarianism? In order to prevent a nationalized healthcare system in the United States, President Nixon

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signed into law the Health Maintenance Organization (HMO) Act of 1973 to reduce the healthcare costs thought to be making US corporations less competitive. The HMOs were also given extraordinary power in medical decisions by reducing their obligation, hence liability, to purely contractual issues. The effect was to blunt any responsibility seen as practicing medicine under any reasonable consideration. In actually practicing medicine, the HMO would be considered liable for medical malpractice lawsuits. Lawsuits alleging medical malpractice against HMOs cannot be brought in state courts, essentially making it extremely difficult to pursue any claim of medical malpractice. The basic legal structure of HMOs was not changed by the Patient Protection and Affordable Care Act of 2010 (otherwise known as ObamaCare), although a certain modicum of covered services was required. Clearly, the political solution is an odd mix of libertarianism and deontological duty with egalitarianism taking a backseat. Within this ever-​ changing and confusing environment of providing healthcare, the clinician understandably tries to come to some equilibrium. The introduction of managed care raised concerns that there would be a hierarchy of care, with those under managed care receiving only “bare bones” care while others received concierge medical care. Surveys demonstrated that a hierarchical system of the intensity or completeness of care did not emerge (Hornbrook and Berki, 1985). However, it is not at all clear that medical practice did not devolve to the lowest common denominator. Physicians have always been caught in the middle and, like anybody else, sought to stabilize their own situation in uncertain times. Capitation of costs have existed since the poor laws in the 1600–​1800s, where the surgeon had to provide his own bandages. A form of block grants and vouchers were seen in the consolidations of parishes without a similar increase in resources. There has been a movement by insurers to engage physicians or healthcare providers as gatekeepers to medical care. It has not always been the case that clinicians have recommended medical treatment in the patient’s best interest as some clinicians have become agents for the best interests of the insurer. Indeed, the need to avoid the financial costs associated with “excessively sick” patients has led to “cherry picking” the least sick.

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The following was originally posted in 2016 as a newsletter at http://​www. greenvilleneuromodulationcenter.com/​: Dear Dr. Montgomery: I did not refer the patient [to you]. I  don’t need your help. Here is your evaluation and recommendations back. Dr. X

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This patient sought a second opinion, as is any patients’ right. The patient asked the evaluation and recommendations be sent to Dr. X, which any physician would be obligated to do. The paradox is that Dr. Montgomery agreed with the diagnosis set forth by Dr. X and only offered one additional consideration in the treatment. Clearly, the description of the event may be out of context and there could be mitigating circumstances, but it is hard to envision circumstances that would justify this response. Apart from not being collegial, it is not in the patient’s best interest. Most clinicians would agree. So, the question for the medical profession is to decide how such incidences are handled, not just for this specific case, but to prevent future occurrences. I have yet to meet any clinician whose first intention was not to help patients. Clinicians are intelligent, hardworking, and likely would find better financial remuneration in fields other than medicine; furthermore, the practice environment is difficult. A very large percentage of physicians would not recommend others to enter the practice of medicine. Yet, students still vie for admission to medical professional schools, so there must be other motivations. Essentially, the question is one of accountability. Nevertheless, one cannot assume that every medical professional holds him-​or herself accountable, and this is sufficient. Many physicians and clinical scientists opposed mandating Institutional Review Boards to govern human research consequent to the Belmont Report in 1978 (Belmont Report, 1978). Many of these physicians and clinical scientists held that good ethical practice is just what good clinicians

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do. The numerous scandals of unethical research, continuing to this day, demonstrate the incredulity of such claims. This is not to say that these physicians and clinical scientists, and by extension medical professionals, intended unethical behavior. Rather, many clinicians may not appreciate the ethical complexities that are exacerbated by the many conflicts of interest imposed on medical professionals. Accountability is part of the ethical practice of medicine. If one considers patients’ rights on par with civil rights, there is no solace even if 99% of patients have their rights respected by physicians who hold themselves accountable. Few professions allow their practitioners such authority with so little accountability. Airline pilots are frequently observed for their flying skills, both in real situations and in simulations. They face mandatory retirement at age 60 in many countries. Many air traffic controllers are required to retire at 56 years. Among the reasons is the risk of sudden medical problems at these ages, placing passengers at an unacceptable risk. Yet there is nothing comparable for clinicians. While US Supreme Court justices have lifetime appointments, they are held accountable, even if only in the public eye. With respect to accountability, courts of law are evolving in the standards by which clinicians are held accountable. Courts of law are departing from definitions of malpractice as departures from standards of care that are determined by the conduct of similarly situated medical professionals to what a reasonable medical professional would do. This evolution is a direct consequence of the realization that the prior standards established by peer practice could be self-​severing, thus shielding medical professionals from accountability. I have seen peer-​review conferences where less than the best professional conduct was deemed standard in order to avoid other medical professionals from being held to the best standard of professional conduct in the future.

Malpractice and Accountability In 2000, the Institute of Medicine reported medical errors causing between 44,000 and 98,000 deaths every year in American hospitals (Kohn et al., 2000). While the percentage preventable is open for debate, it is a serious problem (Brennan, 2000). In 2016, medical errors were estimated to be the third leading cause of death in the United States (Makary and Daniel, 2016). Yet only one in seven adverse effects results in a malpractice claim (Oyebode, 2013). The deterrence of the threat of malpractice accusations would appear inadequate as a means of accountability. Deterrence is only effective if those whose actions are the subject find the deterrence credible. A chair of a medical school department was unconcerned, saying “our patients don’t sue.” Furthermore, rightly or wrongly, most medical professionals view the application of malpractice claims as capricious; consequently, concern for best practice is not a protection.

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The threats of disciplinary actions by state medical boards are most likely in the same situation as medical malpractice claims. In Texas, 28.7% of revocations of medical licenses were due to incompetency or negligence in the years of 1989–​1998 (Cardarelli and Licciardone, 2006). This rate would seem incongruent with the frequency of medical errors as just described. Also, medical malpractice claims, as well as a large number of state medical board actions, require an aggrieved party to make a claim and thus will likely underestimate the extent of the problems, further undermining interest in accountability.

Peer Review Self-​policing has been advocated for accountability. An archetype is the morbidity and mortality conference whose intended purpose is to review cases that went wrong. But studies have shown that the issue of errors in reasoning is rarely addressed (Gore, 2006; Orlander and Fincke, 2003; Pierluissi et  al., 2003), despite the fact that most governments provide legal protection against self-​incrimination. It has happened that a physician, concerned about the conduct of a surgeon and explicitly following the instructions of the departmental chair in seeking accountability, found himself removed from any position to observe the surgeon’s subsequent behaviors and was subjected to retaliation by constructive dismissal. In another situation of unprofessional conduct by a physician in a private practice, another physician attempted to enlist the help of the local medical society to find an informal and least threatening means to address the conduct after direct attempts to personally discuss the situation failed. The medical society said it had no means and referred the concerned physician to the state medical board, which stated it would only intervene if a formal complaint was lodged after speaking to the board’s attorney. Such actions would only escalate the tension and cause the physician of concern to become defensive. Making an opportunity to seek accountability into a “nuclear option”—​ legal action—​effectively takes such interventions off the table.

Too Few to Fail In a free capitalist market, the consumer, in this case the patient, could “vote with their feet” and find a physician willingly accountable. Let the “free hand of the market” force accountability. However, for any free market to be effective, there must be transparency and choice. In this case, consumers (patients) cannot force transparency because of their lack of knowledge compared to the provider. Furthermore, there is really little choice. Whatever choice patients

A Calling to Be Better than Ourselves

may have had has been lost by health maintenance organizations and preferred provider organizations that severely limit the patient’s choice. Any means to enforce accountability are eroded by the shortage of physicians generally. It is estimated that the shortage of neurologists from what is needed will grow from 11% (in 2012) to 19% by 2025 (Dall et al., 2013). The concern is that a healthcare provider system will not risk alienating a neurologist (or any other clinician) by insisting on higher standards of accountability.

Ethical Principles and Moral Theory Ethically and morally, clinicians have a responsibility to hold themselves, and each other, accountable. Such accountability is inherent in the ethical principle of respect for autonomy. Similarly, accountability is inherent in the ethical principle of justice, whether that principle of justice is informed by libertarian or Kantian (deontological) morality. The Kantian notion of morality holds that each person (patient) is an end unto themselves and not a means by which others achieve their ends. Libertarian moral theory holds that the maximum freedom is the greatest good. Libertarianism avoids anarchy by mutually agreed contracts, either implicit or explicit, where benefits and obligations are freely traded with a set of constraints that enforce good faith and fair dealing. Generally, patients (or their legal representatives) have contracts with their clinician. It may be explicit between them or a surrogate who can intervene on the patient’s behalf, such as the insurer, government, or society. For example, the government, such as state medical boards, requires licensed clinicians to treat every patient to the standards of practice. It is not necessary for any patient to negotiate for such treatment. Furthermore, there is a contract between the government and medical professionals representing an exchange of goods and obligations. The government grants medical professionals a form of monopoly. Only those licensed by the government can enjoy the goods that come from providing professional medical care. In exchange, medical professionals are expected to act in the patient’s best interest. The state holds the physician accountable. Physicians and many healthcare professionals have an additional obligation of beneficence to the patient. It is highly unlikely that any physician or healthcare professional paid for his or her education and training on their own. It is society that provides the opportunity for individuals to become clinicians. This implies an obligation to reciprocate by caring for patients as patients deserve. In the end, it is a matter of personal choice by the clinician. All the ethical principles and moral theories, in themselves, cannot compel the best efforts of medical professionals. As discussed, there is very little in the way of effective outside enforcement of accountability. Consequently, the only real recourse is

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internal accountability. In other words, it is up to each clinician to hold himself or herself accountable. Thus, we come back around to the issue of personal motivation. It is hoped that these motivations will have a perspective that is outwardly directed to the benefit of patients. There would be much to gain by such a perspective. It is striking that Mohandas (Mahatma) Gandhi entitled his autobiography describing his efforts on behalf of his fellow citizens The Story of My Experiments with Truth (1948). One can learn a great deal about oneself in the process of committing to the care of others and great joy despite the sorrows. Of Patients Knowing what they are, How can you love them? Their lives shattered like Broken glass on the path they ask you to walk. How can you love them, Knowing what they do? Because I am human I can love, I can choose to do for them And for me.

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INDEX Figures are indicated by an italic f following the page/​paragraph number. abduction. See also Fallacy of Confirming the Consequence; hypothetico-​deductive approach defined, xxx dominance in medical reasoning, 69, 89–​90 Fallacy of Confirming the Consequence, 57, 69 history of, 87–​89 hypotheses as starting point for, 118–​19, ​ 123, 160 inductions from experience, 114–​15 mind/​brain as empty slate, 198 pathology-​as-​disease paradigm,  124 pattern recognition vs., 123 accountability,  222–​23 free market perspective, 224–​25 malpractice and, 223–​24 moral theory and, 225 peer review, 224 actor/​action distinction, xxx, 124, 206 allopathic medicine (rational medicine; regular medicine), 1, 20 accounting for canonical form and deviation from, 76 approaches to diagnosis, 78, 85 ascendency of, 76–​77 consequent uncertainty of variability, 135–​36 contraries, 132 defined, xxx DSM-​5,  126–​27 empirics vs., 17–​19, 24, 27, 30–​31, 54, 76, 78, 88–​89, 122, 126–​27, 132, 217 Fallacy of Confirming the Consequence and, 69 generalization from the particulars, 208–​9 incommensurability, 122 institutionalization of, 89–​90, 133 inverse problem, 24 medicine as science, 93 notions of causation, 9, 17 preference for “objective” tests, 181–​82 reductionism and, 133–​34, 136–​38 science and, 130–​31 variability vs. diversity, 23, ​31

American Institute of Homeopathy, 88, 122 American Medical Association (AMA) Code of Ethics alignment with science, 130, 133, 206 allopaths vs. empirics, 54, 88–​89, 122 appearance of certainty, 2 criticism from colleagues, 200 criticism of, 19, 130 monopolistic practices, 11 prohibition against pharmacists making diagnoses, 21, 80 role of patients, ​11 violation of antitrust laws, 89 foreign medical training for presidents of, 90 Americans with Disabilities Act, 216 analogical reasoning, xiv, xxii, 70, 97 analytic a priori, 198 anatomy-​as-​physiology paradigm, 125,  1​48 Anaximander, 135 animal-​based research, 26, 186–​87, 188, 191, 192 anthropomorphism, 154, 170, 171, 192 antibiotics, 56–​57, 59, 62, 65, 67 happenstance and development of, 86, 137 technology-​then-​question paradigm,  180 anticholinesterases, 70, 160–​61, 162, 215 apothecarists (pharmacists), 11, 21, 80 A Priori Problem of Induction, xxii applying sample-​based inferences whole cloth to individual patients, 112 central tendency, 95 consideration of specific treatments, 134, 217 defined, xxx evolution of hypotheses, 17, 118 Kantian notion of understanding, 22 limiting certainty of induction, 14, 15, 17, 24, 73, 91, 198 metaphors, 163 metaphysics, 131 pattern recognition, 16, 123 Archimedes, 164 Arcimboldo, Giuseppe, 110

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Index Aristotle, 8, 33, 42–​43, 79, 82, 88, 112, 130, 208 ascribing functions to cells, 141 conception of causes, 168–​69 contraries, 82, 84, 85, 135, 150 first principles, 129 four elements, 81, 168 physics, 129 potentiality, 141, 157–​58, 159 arithmetic mean, 107 arsenicals, 86, 180 aspirin, 9, 86, 88, 112, 207 authority, 14 accountability and, 223 conveyed by certainty of understanding, 21–​22 conveyed by suasion, 23 physicians as adjudicating authority, 41 autonomy, ethical principle of respect for, 8​ , 10, 14, 22, 42, 196, 225 average. See mean average man (l’homme moyen), ​95, 106, 189–​90,  217 BAAS (British Association for the Advancement of Science), 39, 1​85 Bacon, Francis, 39, 73, 139 Baconian science allopaths and, 130 Cartesian science vs., 39, 91, 196 defined, xxx empirics and, 91 induction, 73 metaphysics, 131 transcending experience, 198 balkanized medicine, 203 basal ganglia, 75, 77, 128, 131, 145, 146–​49, 151–​52,  158 Basilius, P. A., 202 Bayer chemical manufacturing company, 180 Bayes’ theorem (“Hoof Beats and Zebras”), xxix,​ 53, 62, 212, 215 Bazerman, Charles, 123 Beauchamp, T. L., 14, 30, 220 Belmont Report, 222 beneficence, ethical principle of, 8, 14, 22, 196 medical errors, 25 obligation of, 225 wide differences among modes of thinking among physicians, 25 beriberi, 84 Bernard, Claude, 106 Big Bang theory, 19 Birth of the Clinic, The (Foucault), 122 blind thought, 12, 13 block grants, 11, 221 bloodletting, 80, 84, 86, 143

Bochner, Salomon, 183 Boolean algebra, 65 Boyle, Robert, 184 Bradley, F. H., 197 Brahe, Tycho, 91, 173, 182 breast cancer, 87 mammograms, 45–​46, 50–​52, 53, 220 British Association for the Advancement of Science (BAAS), 39, 185 Broca, Pierre Paul, 144 capitative care reimbursements, 11 cardinal cell hypothesis (grandmother cell hypothesis), 144 Cartesian science, 129–​30, 20​8 allopaths and, 130 Baconian science vs., 39, 91, 1​96 defined, xxii case-​based learning, 77, 115, 200, 206, 207 category error, 49 causal syllogisms, xxiii, 9–​10 causation,  8–​9 causal syllogisms, xxiii, 9–​10 causal theory of disease, 84 hypothesis generation, 10 hypothetico-​deductive approach, 10 induction, 72 reticence to accept the absence of causality, 112 variability vs. diversity, 17 Causational Synonymy. See Principle of Causational Synonymy cause and effect, 6, 10, 112 cell theory, 76, 87, 125, 132, 134, 137, 138–​41, 142–​43, 170, 173 cellular pathology, 124, 125, 142 Centor rules, 59 central tendency, See also mean, 37, 39, 61, 76, 93, 98–​107, 189 chaos and complexity, 44 cumulative percentage (probability) function,  104–​5 distribution of percentage of improvement, 99–​101, 102, 103 metaphysical notion of, 105–​6 mode and median vs., 102 quartiles,  102–​4 types of means, 107 variance vs., 95–​96 certainty,  1–​20 anti-​physician sentiment due to pretentious certainty, 1 banishing fear through, 21 discomfort with uncertainty, 1 extra-​logical considerations,  10–​11 clinician’s self-​concerns, 11

Index ethics, 10 government, 11 privileged knowledge, 11 thresholding, 10 importance of, 1 myth of the inevitability of, 40 objectifying, 1 power of suasion derived by projected sense of, 21 quest for, 1–​2 ramifications of uncertainty allopaths vs. empirics, 17–​18 deduction and its derivatives, 2–​6 discipline of logic, 15–​16 hypotheses,  16–​17 induction,  12–​15 origins of ideas as hypotheses, 16–​17 probability and statistics, 6–​10 science and scientism, 19 utility vs., 5, 7, 20 cervical dystonia, 68, 114–​16, 118 chaos and complexity, 2, 19, 97, 138, 158 defined, xxx dynamics,  174–​77 information-​theoretic incompleteness, 214 living biological systems, 138 “practical” reasoning, 31 self-​organization,  175–​77 three-​body problem, 174 unpredictability, 44 Charcot, Jean-​Martin, 145 Charles II, 184 Charon, Rita, 27, 31, 34 Childress, J. F., 14, 30, 220 chiropractic, 54, 75, 133 cholesterol, 41 cholinergic-​dopaminergic imbalance theory,  145–​46 Church’s theorem, 9 circulation, 84, 134, 136 Clinical and Translational Research Award program, 191 clinical judgment (practical judgment), xxiii. See also practical reasoning clinical meaningfulness, 49, 215–​16 clinical reasoning, 1, 189, 205–​6, 207, 209, 214, 215, 216, 218 Code of Ethics, AMA alignment with science, 130, 133, 206 allopaths vs. empirics, 54, 88–​89, 122 appearance of certainty, 2 criticism from colleagues, 200 criticism of, 19, 130 monopolistic practices, 11 prohibition against pharmacists making diagnoses, 21, 80

role of patients, 11 violation of antitrust laws, 89 Coleridge, Samuel, 184 common morality, xiii, 14, 30, 53, 120 complexity and chaos. See Chaos and Complexity computed tomography (CT), xxiii, 22, 181–​82 conceptual permanence, 15 confidence intervals, 7, 9, 37, 38, 61 confirmation bias, xiii, 123, 193, 213, 218 Confirming the Consequence. See Fallacy of Confirming the Consequence continuous variables, 7, 10, 46, 47, 48, 72 contraries, xxiii, 82, 84, 85, 132, 135, 146, 150 cookbook medicine, xxiv, 24, 134 coronary artery bypass, 53 Critique of Practical Reason (Kant), 204 Critique of Pure Reason (Kant), 22, 28, 129, 204 crystal violet dye, 125 CT (computed tomography), xxiii, 22, 181–​82 cumulative percentage (probability) function, 99, 100, 101, 102, 103, 104–​5 curare, 70, 160–​61, 162, 215 DBS (deep brain stimulation), 43, 84–​85, 108, 160, 162, 195 decision trees, 24, 40 deduction, See also syllogistic deduction, 45–​55,  56–​73 common currency, 52–​53 consequence of failure to uphold Principle of the Excluded Middle,  59–​61 defined, xxx dichotomization based on statistical significance,  49–​50 facts as insufficient, 54–​55 Fallacy of Confirming the Consequence,  66–​68 Fallacy of Pseudotransitivity, 70–​71 hypothetico-​deductive approach,  46–​47 limits of, 47–​48 logical argumentation, 57–​59 patient imperative, 48 power of, 65 Principle of Transitivity, 69–​70 prior probability of relevant positive tests,  61–​62 propositional logic, 3–​4, 5–​6 sensitivity, specificity, and predictive values of tests,  63–​65 deep brain stimulation (DBS), 43, 84–​85, 108, 160, 162, 195 dementia, 120, 220 depression, 41, 83, 85, 126, 136, 170

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240

Index Descartes, 1​29, 169, 196, 198. See also Cartesian science, descriptive approach, 23 actor/​action distinction, 124 ethnographic studies, 206 need for, 30 synergy between normative approach and, 30 descriptive ethics, 40, 197 diabetes, ​23, 25, 78, ​87, 94, 180, 219 Diagnostic and Statistical Manual (DSM-​5), 19, 126–​27,  206 dichotomous decisions, See also one-​dimensional push-​pull dynamics, 7, 36, 46, 48, 59, 64, 76 dichotomous variables, 82 differential diagnosis defined, xxx ethical considerations, 120 hypothesis generation, 119, 124 if and only if logic, 67, 119 inverse problem, 77–​78 rational approach to hypothesis generation, 124 digitalis, 86 digoxin, 207 direct method of agreement in induction, 71 disciplinary actions by state medical boards, 224 diversity defined, xv, 76, 93 economical sets of mechanisms and explanations, 159 empirics and, 18, 79, 96 inverse problem, 105 reductionist vs. inductive approach, 20 variability vs., 23, 27, 30, 76, 93–​97, 98, 217 “Doctor in Spite of Himself, The” (“Le Médecin malgré lui”) (Molière), 1 dogmatics (methodists), xxiv, 2, 23, 87, 122, 134 Domagk, Gerhard Johannes Paul, 180 Donne, John, 21, 218 “Don’t Change Too Many Things at Once Lest You Get Confused,” xxiv, 31 dopamine cholinergic–​dopaminergic imbalance theory,  145–​46 depression, 136, 170 Parkinson’s disease, 18, 25, 85–​86, 112, 116, 136, 145, 148, 201 schizophrenia, 83, 136 Driesch, Hans, 169 DSM-​5 (Diagnostic and Statistical Manual), 19, 126–​27,  206 Duhem-​Quine thesis, xxv, xxxi, 24, 77, 191, 193 dynamics,  167–​77

attribution of function, 170 chaos and complexity, 174–​77 forces that give rise to, 168–​69 mathematical metaphors, 173 one-​dimensional push-​pull dynamics, 82–​83, 131–​32, 133, 135, 146, 148, 150–​52, 153–​54,  171–​72 prime mover/​first cause, 169 telos, 168–​69, 170 economical sets of mechanisms and explanations allopaths vs. empirics, 18–​19, 24, 30–​31, 76, 78, 132, 217 dichotomizing forces and entities, 82 economical, defined, xxv inverse problem and, 24 reductionism, 133, 135, 138 variability vs. diversity, xv, 23, 96, 159 Ehrlich, Paul, 125, 180 Einstein, Albert, 156 eliminative materialism, 206 empiric medicine (irregular medicine), 1, 9, 20 allopaths vs., 17–​19, 24, 28, 30–​31, 54, 76, 78, 88–​89, 122, 126–​27, 132, 217 AMA’s early perspective on, 54 approaches to diagnosis, 78, 79, 85 defined, xxx DSM-​5,  126–​27 history-​taking,  217 incommensurability, 122 medicine as art, 93 metaphysics, 134 multiple canonical forms, 76 no explicating theory of causation, 17 science and, 91 variability vs. diversity, 23, 30 Employee Retirement Income Security Act (ERISA), 22 Enlightenment, 11 entropy, xxxvi, 189 enumeration by counting, 71 epistemic condition, xxv, 23, 96–​97 epistemic conundrum, xiv, xv, xvi, 98, 132. See also variability vs. diversity allopaths vs. empirics, 27 contraries, 84, 132 induction, xvi epistemic degrees of freedom, xxv, 35, 52, 1​62, 169, 192 epistemic distance, xxvi, 35, 52, 119–​62, 169, 192 epistemic perspective, 2, 17–​18, 19, 20, 36. See also allopathic medicine; empiric medicine–​44 applied epistemology, 29

Index clinicians’ obligation to reason, 40–​41 different medical approaches, 76 evolution of, 27–​28 evolution of medical reasoning and misreasoning,  41–​43 myth of the inevitability of certainty, 40 necessity of action as evolutionary force, 36–​37 philosophical analyses, 43 universal scientific judgment vs. particular common sense judgment, 37–​39 epistemic risk, xxvi, 35, 52, 111, 119, 161, 178, 183, 192, 194, 219 ergodicity, 113 ERISA (Employee Retirement Income Security Act), 22 ethics accountability,  222–​25 clinical meaningfulness, 216 codes of (see also Code of Ethics, AMA) common morality, xiii, 14, 30, 53–​54 descriptive vs. normative, 40 influence on differential diagnoses, 120 knowledge of, 195 logic of, 7–​8 morality vs., 220 philosopher ethicists, 29 as undue burden and restraint, 19 Euclid’s fifth postulate, 18 evidence-​based medicine. See also randomized controlled trials (RCTs) Baconian science, 73 converting questions to explicit probability syllogisms, 9 defined, xxvi resistance to, 37, 122 reticence to accept absence of causality, 112 schism between allopaths and empirics, 122 solipsism, 15, 200–​1 statistical inference, 207 variability, 94 evolutionary epistemology, xxvi, 27–​28, 44, 110 evolutionary logic, xxvi, 40, 110 Excluded Middle. See Principle of the Excluded Middle existential quantifiers, 5 experimentalism, 39, 96, 183–​85 extrapolation defined, xxvi, 22, 121 epistemic vs. ontological questions, 45 hypothesis generation, 121, 122 Fallacy of Composition, 141, 146 Fallacy of Confirming the Consequence, xxvii. See also abduction; hypothetico-​deductive approach; scientific method, 3, 12, 66–​67

avoiding,  67–​68 defined, xxvii differential diagnoses, 78 Fallacy of Limited Alternatives leading to, 68 hypothesis generation, 17, 24, 122 inverse problem, 77–​78 irreproducibility, 193 used in modus ponens-​like manner, 3 Fallacy of Denying the Antecedent Fallacy of Division, 141 Fallacy of Four Terms, 4–​5, 15, 34, 52, 63, 70, 85, 105, 145, 152, 169, 194, 206, 217, 218 epistemic risk, 161, 192, 219 irreproducibility, 29, 189, 194 lack of utility of test sensitivity, 63 randomized controlled trials, xxxv, 9, 31, 51, 112 reductionism, 133 representative samples, 105 as source of new knowledge, 5 Fallacy of Induction, xxviii, xxx, 14, 71, 131 Fallacy of Limited Alternatives, 31, 68, 79, 115, 142 defined, xxxiv failure to diagnosis, 29 incommensurability, 123 inverse problem, 24, 77–​78 Fallacy of Pseudotransitivity (metaphor), xxviii, 9, 12, 17, 43, 116, 131, 146, 160, 180, 217 centrality of hypotheses, 160 defined, xxxv, 161 epistemic risk, 192 hypothesis generation, 9, 17, 24, 29, 32, 111, 119, 160, 162, 215 leading to errors, 162–​63 mathematical metaphors, 173–​74 in medical and scientific knowledge, 161–​62 off-​label use of drugs and devices, 70–​71 process metaphors examples of, 164–​65 reductionism as, 166 scientific method, 208 as structuring observation, 163 as syllogisms, 162 FDA. See US Food and Drug Administration Feyerabend, Paul, 121, 146 first principles, 39, 129 5 sigma, 38, 49 Flexner Report and Flexnerian revolution, 18, 19, 25, 75, 76, 89–​90, 130, 133, 185, 207 Flourens, Pierre, 144 fluorescent treponemal antibody (FTA) test, 219 fluoride, 49 Foucault, Michel, 122 Four Terms. See Fallacy of Four Terms

241

242

Index From Detached Concern to Empathy (Halpern), 27, 31 frontal lobes, 132 antisocial behavior, 128, 142, 153, 154, 170 cognitive and personality disorders, 83 FTA (fluorescent treponemal antibody) test, 219 Full House (Gould), 28 functionalism, 97 fuzzy logic, xxix, 6, 44, 68 gain/​loss of function, 82, 84, 133, 150 Galen, 42–​43, 79, 81, 135 abduction, 87 Aristotelian influences, 81–​82, 87 four humours, 81, 82 Galenic medicine, 42–​43, 79, 84, 86, 87, 96, 133, 135, 150 Galileo, 125, 128–​29, 130, 164, 173 Gall, Franz Joseph, 144, 170 Galton, Francis, 95–​96 Gambler’s Fallacy, xxix defined, xxxiv differential diagnosis, 78 failure to diagnosis, 29 Fallacy of Limited Alternatives leading to, 24, 68 Gandhi, Mohandas, 226 general principles abduction, 114, 115 Baconian science, 91 clinical reasoning, 207 conceptual permanence, 15 defined, 65 enumeration by counting, 71 induction, xxx, 13, 53, 71, 114, 120 A Priori Problem of Induction, 15 scientific knowledge, 205 geometric mean, 107 germ theory, 39, 86, 134, 150 Gestalt psychology, 110 Getzendanner, Susan, 54 globus pallidus interna and globus pallidus interna rate theory, 43, 83, 87, 128, 131, 146, 147, 148, 158, 162, 170 Gödel’s incompleteness theorems, 2, 12, 18, 19, 121, 138, 214 Goldbach’s conjecture, 214 Golgi, Camillo, 143 Gould, Steven J., 28 Gram stain, 125 grandmother cell hypothesis (cardinal cell hypothesis), 144 Greenville Neuromodulation Center, xix Groopman, Jerome, xvi, 27, 32, 206

Guttmann, Paul, 180 Haeckel, Ernst, 139 Hahnemann, Samuel, 54, 88 Halley, Edmund, 91, 173, 182 Halpern, Jodi, 27, 31, 34 halting problem, 12, 214 Harvey, William, 84, 134, 136 headaches, 62, 120, 181, 182, 217 Health Maintenance Organizations (HMOs), 22, 221, 224 Hegel, Georg Wilhelm Friedrich, 82 Heisenberg uncertainty principle, 2, 12, 213 herbalists, 54, 75 hierarchical conceptualization, 139, 151–​53 Higgs boson, 36, 37, 38, 49 Hippocrates, 21, 86 Hippocratic Oath, 21, 80 histopathology, 76, 90, 125, 132, 134, 142–​43, 169, 217 history and evolution of medicine, 75. See also allopathic medicine; empiric medicine–​91 abduction history of, 87–​89 reinforcement of, 89–​90 allopathic medicine, 75 ascendency of, 76–​77 institutionalization of, 89–​90 diagnosis vs. treatment, 80 empiric concept of science, 91 Galen and, 81–​83 persistence of Galenic ideas, 84–​86 historical approaches to diagnosis, 78–​79 inverse problem, 77–​78 mechanistic theories, 87 relationship with science, 79–​80 history-​taking, 118,  217–​18 HMOs (Health Maintenance Organizations), 22, 220, 224 Hobbes, Thomas, 128–​29, 139, 184 Hodgkins, A., 173 homeopathy, xxix, 19, 54, 69, 75, 78, 87–​88, 89, 93, 122. See also empiric medicine, “Hoof Beats and Zebras” (Bayes’ theorem), xxix, 31, 52, 62, 212, 215 Hooke, Robert, 125, 138 How Doctors Think (Groopman), 27 Human Genome Project, 83, 137 Hume, David, 73 Huntington’s disease, 69–​70, 83, 116, 131, 144, 178 Huxley, A., 173 hyperporcelainemia (hypothetical condition), 36, 38, 47

Index hypertension, 41, 86, 94, 125 hypotheses,  118–​27 causation, 9 differential diagnosis, 119, 124 DSM-​5 and allopaths vs. empirics, 126–​27 extrapolation, 121, 122 Fallacy of Confirming the Consequence, 17, 24, 122 Fallacy of Pseudotransitivity, 9, 17, 24, 29, 32, 111, 119, 160, 162, 215 generation of, 24 importance of, 119–​20 interpolation, 121, 122, 160 intuition,  111–​13 knowledge and, 24 logical structure giving rise to, 17 origins of, 120, 125 pathology-​as-​disease paradigm,  124–​25 pattern recognition and, 16, 123–​24 postmodernism and, 121–​23 rational approach and, 124 sources of, 17 transcendence of moment, 16 hypothetico-​deductive approach causation, 9 defined, xxxii, 46 Fallacy of Confirming the Consequence, xxvii, 3, 29, 49, 67–​68, 208, 218 hypotheses, 16, 24, 119 modus ponens-​like version of deduction, 3 notions of causation, 9 pattern recognition approach vs., 123–​24 propositional logic, 46 syllogistic deduction, 47 ICD (International Statistical Classification of Diseases and Related Health Problems), 18, 127 incommensurability,  122–​23 induction, See also A Priori Problem of Induction; Fallacy of Induction, 13–​15,  71–​72 attempts to validate arguments by, 58 Baconian science, 91 causation, 72 conundrums limiting the certainty of, 14 defined, xxix, 13 from experience, 58, 114 generation of logic for ethical and legal principles, 14 generation of propositions, 13 limits of, 72, 198 metaphysics, 16 methods of, 71–​72 new knowledge vs. certain knowledge, 71

Principle of the Excluded Middle, 46 scientific method and, 73 synthetic a priori, 22 induction by enumeration, 13 inertia, 164 inevitability of uncertainty, 40 information, defined, xxx information, loss of. See Second Law of Thermodynamics as Applied to Information Informational Synonymy. See Principle of Informational Synonymy information-​theoretic incompleteness, 214 inhibition, misperception of, 154–​57 Insel, Thomas R., 19, 126–​27, 188, 190, 206 instantaneous velocity, 165 Institutional Review Boards (IRBs), 202, 222 instrumental variance and noise, 105–​6 International Statistical Classification of Diseases and Related Health Problems (ICD), 18, 127 interpolation defined, xxxi, 22, 121 epistemic vs. ontological questions, 45 hypothesis generation, 121, 122, 160 potential contributions of science, 183 theories and, 120, 128 utility of, 38 intuition, 87, 108, 206, 209–​13 “given” vs. “intellected” perceptions, 108–​10 “gut feeling,” 211 humans “wired” for, 110 hypothesis generation, 111–​13 non-​evidence-​based methods of medical reasoning, 116 probability and statistics applied to, 111–​13 applying sample-​based inferences, 112 complexity and variability of disease,  111–​12 ergodicity, 113 sample size and biased sampling, 113 suspiciousness for bias, 112 testimonials, 111 RCTs vs. case series, 113–​14 recognition, 211 undersampled and biased, 114–​16 inverse problem, xxxi, 24, 31, 77, 78–​79, 119 contraries, 132 defined, xxxi history-​taking and physical examination, 218 metaphysical presuppositions, 142–​43 multiple causes of the same phenomenon, 218 testimonials, 111 variability vs. diversity, 105 IRBs (Institutional Review Boards), 202, 222 irregular medicine. See empiric medicine

243

244

Index irreproducibility, 2, 150, 186–​94 animal-​based research,  186–​87 areas to explore, 191–​93 false negatives, 187 fraudulent research, 187 information loss and, 189–​90 lack of transparency and poor statistical techniques, 26 magnitude of, 186–​87 in medical studies and treatments, 26–​27, 29 recalls, 26, 34, 187 reconstruction, 194 scope of the issue, 188 Jackson, Andrew, 88 Jackson, John Hughlings, 84 Jacobi, Abraham, 130 Jenner, Edward, 86 joint method of agreement and difference in induction, xxxi, 72, 105, 131 justice, principle of, 8, 14, 196, 225 Kant, Immanuel, 22, 28, 111, 129, 196, 198, 204, 225 Kepler, Johannes, 91, 131, 173 Klein v. Biscup, 202 knowledge, 195. See also Fallacy of Confirming the Consequence; Fallacy of Four Terms; induction; scientific method–​97 certainty and, 20, 23 consequences and, 196 gaining new, xxxiii, 3, 12, 23, 24, 71, 111, 136, 198 hypothesis generation and, 24 information and, 196–​97 nonrandom acts and, 196 understanding vs., 22 Koch, Robert, 86 Kostopoulou, O., 210–​11, 213 Kövecses, Z., 161 Kuhn, Thomas, 54, 121, 146, 183 Landis, Story, 188, 190 large numbers theorem, 44 Lebiniz, G. W., 165 Leeuwenhoek, Antonie Philips van, 125, 138 Leibniz, Gottfried Wilhelm von, 12 “Le Médecin malgré lui” (“The Doctor in Spite of Himself ”) (Molière), 1 Leppmann, Arthur, 180 lesioning of neuophysiological structures, 152–​53, 168, 180 Leviathan (Hobbes), 139, 140 levodopa, 25, 85, 87, 116, 145, 201

l’homme moyen (average man), 94, 106, 189–​90,  216 Lieberman, Jeffrey A., 127 Limited Alternatives. See Fallacy of Limited Alternatives Locke, John, 198 logic. See also deduction; induction conflating with science, 32 discipline of, 15–​16 evolutionary logic, 27–​28 science vs., 32–​33 solipsism vs., 15 use and misuse of logical errors, 29 Logical Empiricists, 12 logical fallacies, 20. See also names of specific fallacies gaining new knowledge through, xxvii, 20, 23 power of logic, 12 scientific method as, 4 value in hypothesis generation, 24, 34 Logical Positivists, 12, 121, 198 Lorenz, E., 174 Lorenz attractor, 174–​75 lower motor neurons, 147, 151, 218 macroneuron approach, 146, 147, 148, 163 magnetic resonance imaging (MRI), 62, 115, 126,  181–​82 Making Medical Knowledge (Solomon), 122 malaria, 125, 180 mammograms, 45–​46, 50–​52, 53, 220 Maslow, Abraham, 83 mathematical metaphors, 173–​74 Maxwell’s laws of electromagnetism, 39 Mayberg, Helen, 85 Mayr, Ernst, 169 mean (average), 98–​107 cumulative percentage (probability) function,  104–​5 distribution of percentage of improvement, 99–​101, 102, 103 as factitious construct, 94 metaphysical notion of, 105–​6 mode and median vs., 102 as philosophical form, 94 quartiles,  102–​4 types of, 107 mean (average) vs., 102 metaphysical notion of, 105–​6 Medical College Admission Test, 91 medical errors, xvii, 25, 41, 199 diversity of modes of thinking, 25 evolution of misreasoning, 41–​43 malpractice and accountability, 223–​24 medical intuition. See intuition

Index Mereological Fallacy, xxxi, 141, 144, 163 metaphor. See Fallacy of Pseudotransitivity metaphysics, 17, 79, 96, 122, 128–​48, 184 allopaths and reductionism, 133–​34 allopaths and science, 130–​31 case study of neurological disorders of movement,  144–​48 cell theory, 138–​41 centrality of, 128–​30 contraries, 132 dogmatics, 134 empirics, 134 inverse problem, 142–​43 Mereological Fallacy, 141 neurology and the neuron doctrine, 143–​44 reductionism, 133–​34,  136–​38 role of theory in shaping observation,  131–​32 social-​political theory,  138–​39 triumph of allopathic medicine, 133 variety and its consequent uncertainty, 135–​36 methodists (dogmatics), xxiv, 2, 23, 87, 122, 134 method of concomitant variations in induction, 13, 72 method of difference in induction, 30, 72, 142 method of induction, 30 method of residue in induction, 72 methodological reductionism, 136, 138, 148,  152–​53 methylene blue, 180 metonymies defined, 163 as structuring observation, 163 Mill, John Stuart, 13, 30, 72, 105, 131 misreasoning, evolution of, 41–​43 modal logic, 5 mode, 102 modus ponens, xxvii, 3, 12, 61, 66 modus ponens-​like version of deduction, 3, 58 modus tollens, xxxii, xxxvi, 4, 12, 61, 66 Moivre, Abraham de, 182 Molière, 1 Montgomery, Erwin B., III, xix Montgomery, Kathryn, 27, 31, 32, 34 moral theory, xiii, 120, 215, 225 Morris, D. B., 30 MRI (magnetic resonance imaging), 62, 115, 126,  181–​82 multiple sclerosis, 113, 116, 160 myasthenia gravis, 70, 116, 160–​61, 162, 178, 215 Narrative Medicine (Charon), 27, 31 National Center for Advancing Translational Sciences (NCATS), 179, 180, 191

National Institute of Biomedical Imaging and Bioengineering, 179 National Institute of Mental Health (NIMH), 19, 127, 188, 206 National Institute of Neurological Disease and Stroke, 188 National Institutes of Health (NIH), 25, 179, 183, 191 NCATS (National Center for Advancing Translational Sciences), 179, 180, 191 Necker cube, 109, 172 negative predictive value, xxxii, 59, 61, 63, 64–​65, 79, 126, 181, 217, 218, 219–​20 Neurology (journal), 122 neurometabolic imaging, 83, 126, 154, 170 neuron doctrine, 87, 132, 143–​44 Newton, Isaac, 39, 91, 130, 164–​65, 173, 174, 182, 207 Nietzsche, Friedrich, 121 NIH (National Institutes of Health), 25, 179, 183, 191 NIMH (National Institute of Mental Health), 19, 127, 188, 206 1984 (Orwell), 146 Nixon, Richard, 220 nonmaleficence, ethical principle of, 7–​8, 14, 196 norepinephrine, 83, 170 normative approach, xiv, 23 actor/​action distinction, 124 ethnographic studies, 206 need for, 30 synergy between descriptive approach and, 30 normative ethics, 40, 197 Novum Organum (Bacon), 73 number-​theoretic incompleteness, 214 ObamaCare (Patient Protection and Affordable Care Act), 22, 203, 220 “objective” tests, 1, 181 object permanence, 15–​16 obligation to reason, 40–​41 obsessive-​compulsive disorder, 126 Occam’s Razor (Principle of Parsimony), xxxiv, 121, 213, 215 off-​label use of drugs and devices, 9–​10, 116,  202–​3 Fallacy of Pseudotransitivity, 69, 160 technology looking for a question, 181 one-​dimensional push-​pull dynamics, 82–​83, 131–​32, 133, 135, 146, 148, 150–​52, 153–​54,  171–​72 ontological reductionism, 136, 152, 206 Oregon Plan, 216 Orwell, George, 146

245

246

Index Osler, William, 76, 79, 84, 86, 90, 93, 134, 142–​43, 169, 207 osteopathic medicine, 75, 93, 133 pallidotomies, 43, 87, 162 parkinsonism, 144–​45, 148 Parkinson’s disease, 18, 25, 43, 67–​68, 75, 77, 82, 85–​86, 87, 112, 116, 119, 131, 136, 144–​48 Parsimony. See Principle of Parsimony partial syllogisms, 6–​7, 8, 10, 20, 47, 60, 61 defined, xxxii state-​of-​being relationships,  8 statistics and, 10 part-​whole metonymies, 163 pathology-​as-​disease paradigm,  124–​25 pathophysiology, 54, 77, 82, 87, 125, 134, 143 Patient Protection and Affordable Care Act (ObamaCare), 22, 203, 2​21 pattern recognition, xxxii, 16, 30, 46, 88, 123–​24,  208 peer review, 2, 16, 187, 203, 224 penicillin, 86, 116 Perony, Nicolas, 176 pharmacists (apothecarists), 11, 21, 80 Philosophiæ Naturalis Principia Mathematica (Newton), 130 philosophical analysis, 43 Philosophical Transactions of the Royal Society, 123 phrenology, 144, 170, 171 physical-​theoretic incompleteness, 213 physical therapists, 80 Physics (Aristotle), 129 plasmapheresis, 116 Plato, 109 Poincaré, Henri, 174 Poor Laws of England, 11 Popperian demarcation of science, 32 population, defined, 34 positive predictive value, xxxiii, 61, 64, 219 positivism, 12, 121, 198 postapproval recall, 26, 29 postmodernism, 11, 30, 121–​23 potentiality, xxxiii, 141, 154, 156–​59 practical judgment (clinical judgment), xxxiii. See also practical reasoning, practical reasoning, 1, 31, 38, 49, 52, 205–​6, 208, 209, 214, 215 defined, xxiv, 205 inverse problem, 77 limits of science and scientific reasoning necessitating,  213–​15 partial syllogisms, 60 practical syllogisms, xxxiii, 6, 8–​10, 17, 20, 33, 50, 51 prime mover, 169, 184

Principle of Causational Synonymy, xxxiv, 10, 111, 169 Principle of Informational Synonymy, xxxiv, 10 Principle of Parsimony (Occam’s Razor), xxxiv, 121, 213, 215 Principle of Sufficient Reason, 169 Principle of the Excluded Middle, xv, 46, 48, 59, 61, 68, 71 certainty of propositional and syllogistic deduction, 6 computer operations, 65 consequence of the failure to uphold, 59–​61 defined, xxxiv, 5 efforts to mitigate or escape, 5 paradox of attempted escape from, 7–​8 Principle of Transitivity, xv, 12, 17, 32, 57, 69, 116, 139 prior probabilities, 60–​63, 64, 78, 79, 119–​20, 124, 217, 218–​19 probability,  6–​10 defined, xxxiv improving certainty by introduction of, 7 improving confidence in probability premise by introduction of, 7, 10 statistical confidence and, 48 statistics and, 7 thresholding, 8 probability (cumulative percentage) function, 99, 100, 101, 102, 103, 104–​5 probability density function, 100 probability syllogisms, 7, 9, 10, 48, 60, 61, 67 cause of error in, 7 Church’s theorem and, 9 defined, xxxv introduction of statistics, 7 range of possible values, 8 validity of, 7 problem-​based learning, 77, 115, 199, 207 process metaphor chaos and complexity, 174 defined, xxxv, 161 examples of, 164–​65 reductionism as, 166 prontosil, 125, 180 propositional logic, 3–​4, 5–​6, 10, 44, 69 certainty of, 5, 6 defined, 3 hypothetico-​deductive approach and, 47 modus ponens form of, 3 utility of, 12 variables and operators, 65 variations on, 6 Pseudotransitivity. See Fallacy of Pseudotransitivity p value, 49

Index quadratic mean, 107 quality-​adjusted life years (QALY), 53, 216 quantum superimposition, 156 quartiles,  102–​4 quasi-​facts, xxxv, 85, 105, 122–​23, 131 Quine, Willard Van Orman, 97 quinine, 86, 207 Ramon y Cajal, Santiago, 143–​44 randomized controlled trials (RCTs), 94, 187 case series vs., 113–​14 clinical meaningfulness, 215, 216 converting questions to explicit probability syllogisms, 9 defined, xxxv information loss, 189 intuition, 112 limits imposed by Fallacy of Four Terms, 9 risk for bias, 113–​14 schism between allopaths and empirics, 122 rational medicine. See allopathic medicine reasoning, 21. See also clinical reasoning; practical reasoning; scientific reasoning–​35 applied epistemology, 29 certainty, knowledge, and understanding,  22–​24 clinicians’ obligation to reason, 40–​41 critique of, 204–​21 demarcating instances of clinical intuition from,  209–​13 distinctions between science and, 205–​9 evolution of, 27–​28, 41–​43, 75–​91 fundamental limits of, 213–​15 irreproducibility in medical studies and treatments, 26 medical errors and diversity of modes of thinking, 25 non-​evidence-​based methods of, 116 scientific reasoning vs. medical reasoning,  37–​39 synergy between descriptive and normative approaches, 30 use and misuses of logical errors, 29 wide differences among modes of thinking among physicians, 25 recall bias, 116 reconstruction, xxxvi, 136–​37, 138, 188, 194 reductionism, 188 allopaths and, 133–​34, 136–​38 defined, xxxvi limits of, 138 loss of dynamics with, 137

methodological reductionism, 136, 138, 148,  152–​53 ontological reductionism, 136, 152, 206 part-​whole metonymies, 163 as process metaphor, 166 reducing variability, 135–​36 theoretical reductionism, 136, 148, 151–​53 types of, 136 value of, 136 Rees, Martin, 90 regular medicine. See allopathic medicine reserpine, 85, 116, 145 reticularist theory, 143 Revell, Melissa, xix Rikers, R. M., 213 Riley, J. B., Jr., 202 Role of Mathematics in the Rise of Science, The (Bochner), 183 Rose case and Rose Act, 21, 80 Royal Society, 39, 90, 183–​84 Rush, Benjamin, 80, 82, 84, 134, 136, 150 Russell, Bertrand, xvii samples, defined, 34 scans without evidence of dopamine depletion (SWEDD),  85–​86 schizophrenia, 83, 136 Schleiden, Matthias Jakob, 138 Schmidt, H. G., 213 Schwann, Theodor, 138 science. See also history and evolution of medicine allopaths and, 130–​31 art vs., 30–​31 conflating logic and its extensions with, 32 demarcating instances of clinical intuition from,  209–​13 epistemology vs., 34–​35 fundamental limits of, 213–​15 as insufficient to inform the practice of medicine, 34 irreproducibility in, 26, 186–​94 limits of, necessitating practical reasoning,  213–​15 logic vs., 32–​33 mathematization of, 182–​83 potential contributions of, 183 within realm of epistemology, 34–​35 reasoning vs., 205–​9 relationship of medicine and, 79–​80, 130–​31 scientism vs., 19 as technology, 183–​85 technology vs., 178–​85 “science wars,” 121 scientific hubris, 19

247

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Index scientific method, 208 defined, xxxvi Fallacy of Confirming the Consequence, 17, 29, 32, 44, 122, 208 Fallacy of Pseudotransitivity, 32, 208 hypotheses and, 9, 24, 32 induction and, 73 modus tollens form of, 4 science vs., 205 technology-​then-​question paradigm vs., 179 scientific reasoning, 1, 14, 204, 205–​8, 209–​10, 212–​13, 215, 216 limits of, necessitating practical reasoning,  213–​15 medical reasoning vs., 37–​39 scientism, xvii, 19, 20, 34, 133, 207, 218 Second Law of Thermodynamics as Applied to Information, xxxvi, 29, 39, 137, 148, 166,  189–​90 self-​organization, 155,  175–​77 sensitivity of tests, 61, 63–​65, 86, 217, 219 sequestration of patients, 115 shades of gray, 82 Sherrington, Charles, 151, 152, 166, 168, 170 Shonesy, B. C., 192, 193 Snow, John, 86 snowflake formation, 150, 154–​57 Socrates, 1 solipsism, 15, 195–​203 of balkanized medicine, 203 defined, xxxvi of evidence-​based medicine, 200–​1 evidence of knowledge in nonrandom acts, 196 of FDA approval, 202–​3 knowledge and consequences, 196 nature of, 197–​99 object and conceptual permanence, 15 postgraduate medical education and threat of malpractice, 199–​200 problems with, 15 standards of care, 197 Solipsism of the Present Moment, 15, 18, 132, 134 Solomon, Miriam, 30, 122 specificity of tests, 61, 63–​65, 67, 85, 217, 219 Sperry, Roger, 154 standard deviation, 101, 102, 106, 189 standards of care, 197, 199 state-​of-​being relationships, 33, 50 partial syllogism and, 8 Principle of the Excluded Middle, 8 in propositional logic, 3, 5 in syllogistic deduction, 5 statics, 168

statistics, 207 dichotomization based on statistical significance,  49–​50 improving confidence in probability premise by introduction of, 7, 10 mean as factitious construct, 94 mean as philosophical form, 94 probabilities with high statistical confidence, 48 statistical errors, 41 Stigler, Stephen M., 95 Story of My Experiments with Truth, The (Gandhi), 226 strep throat, 56–​61, 65–​67, 71 stroke, 94, 162, 189, 216 Structure of Scientific Revolutions, The (Kuhn), 54, 121 substantia nigra pars compacta, 75, 77, 116, 144–​45, 147, 148 sulfanilamides, 125 sulfonamides, 87, 180 SWEDD (scans without evidence of dopamine depletion),  85–​86 syllogistic deduction, See also names of specific syllogisms, 4–​6, 12, 13, 44, 51–​52 certainty of, 5, 6 creation of through induction, 13 defined, xxxvii, 4 Fallacy of Four Terms, 4–​5 general principles, 65 hypothetico-​deductive approach, 47 limits of deduction, 49–​50 statistics and, 10 variations on, 6 synthetic a priori, 22, 110, 129, 196 syphilis, 86, 180, 219 T0 program, 191 tardive dyskinesia, 69–​70, 115, 116 tautology, xxxvii, 3, 4, 13, 73, 86, 139, 161 Taylor expansion series, 165 team-​oriented approaches, 41 technology experimentalism and science as, 183–​85 mathematization of science, 182–​83 potential contributions of, 183 science vs., 178–​85 technology-​then-​question paradigm,  179 examples from the past, 180–​81 preference for “objective” tests, 181–​82 telos, 168–​69, 170 tetrabenazine, 69–​70, 116 thalamotomies, 162 theoretical reductionism, 136, 148, 151–​53 theories, 120, 128, 129, 131–​32

Index Thiel College, xix Thomasonians, 86 three-​body problem, 174, 194 thresholding, 8​ , 10 throat cultures, 56, 58, 59, 60, 65, 67, 71 tobacco use, 6–​7, 47–​48 Tolstoy, Leo, 142 Transcendental Apprehension, 22 Transcendental Deduction, 22 translational research, 191 Tretiakoff, Konstantin Nikolaevich, 145 Turing, Alan, 12, 214 Tuskegee Syphilis Study, 20 Twain, Mark, 186 type I errors, xxxvii, 26, 187, 191 type II errors, xxxviii, 26–​27, 187, 191 understanding, 129 authority conveyed by certainty of, 22, 23 Kantian notion of, 22, 129 knowledge vs., 22 metaphors in, 161–​63 notion of the synthetic a priori, 22 theories as product of, 22 universal quantifier, 6 US Food and Drug Administration (FDA) gene therapies, 83, 137 off-​label use of drugs and devices, 9, 70, 181, 202 randomized controlled trials, 116 recalls, 26, 187 solipsism of FDA drug and device approval, 15,  202–​3

US Institute of Medicine, 25, 26, 223 US Preventive Services Task Force, 51 US Supreme Court, 54, 89 vaccinations, 19, 86 variability central tendency vs. variance, 95–​96 consequent uncertainty of, 135–​36 contraries, 132 counterbalancing, 94 defined, xv, 76, 93 diversity vs., 23, 27, 30, 76, 93–​97, 98, 217 evidence-​based medicine and RCTs, 94 mean as factitious construct, 94 mean as philosophical form, 94 venereal disease research laboratory (VDRL) test, 219 ventrolateral thalamus, 147, 162 Virchow, Rudolf, 124, 138, 139, 142 vitalism, 125, 157, 169 vital stains, 180 vitamin B12 deficiency, 120, 220 vivisection, 137, 168 Walker, Mary, 70, 160, 215 Walshe, F. M. R., 84, 137, 142 Washington University in St. Louis, xviii Whewell, William, 39, 1​22, 184–​85, 196 White, Arthur, xix Wilk v. American Medical Association, 133 Woolley, A., 210–​11, 213 z scores, 101

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