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It is clear that none satisfy all of the desiderata of a complete and robust philosophical account that also can be useful for practitioners; although some would dispute whether the latter should be a requirement, many believe that philosophy of medicine should be responsive to and helpful for actual clinical practices. Some disease categories are far from straightforward in terms of being recognized, named, classified, and made legitimate both within medicine itself and for the wider society.

In recent times there have been long-standing debates over a range of conditions including Lyme disease, fibromyalgia, and chronic fatigue syndrome CFS , to name just a few for extended historical discussions of these and related conditions, see Aronowitz , ; Shorter Take CFS as an example: its main symptoms are fatigue after exertion over a period lasting at least six months, but sufferers can have a wide array of complaints in diverse systems of the body; the range of severity is as wide as the range of symptoms.

The condition has been associated with several other controversial syndromes and sometimes equated to with them, most notably myalgic encephalitis and fibromyalgia, as well as other illnesses of inexact definition such as multiple chemical sensitivity and irritable bowel syndrome; more popular and derogatory labels also have been attached to it such as yuppie flu.

Definitive evidence as to the cause or basis of CFS has remained elusive, and in the absence of causal explanations, accurate diagnoses and effective treatments often have been difficult to obtain. Thus the illness has been perceived by many as being illegitimate because of difficulties in proving the existence of a discrete disease condition, given the lack of traditional forms of clinical evidence for it, and it has had different statuses in different locales see Ankeny and Mackenzie These issues severely impact on the lives of those affected by this condition, and on the care that is thought to be appropriate to be made available to them.

Many authors advocate the case that it is critical to make a distinction between mental and physical illness Macklin , particularly because of the moral implications associated with labeling a condition as mental or psychological. Psychiatry is a field which has historically been loaded with value judgments, many of which were quite dubious. There is a long history of using mental illness as a way to categorize behaviors which are socially deviant as well as those conditions of ill health with no apparent organic cause and which do not otherwise fit into our dominant biomedical model.

Many scholars e. Key examples of contested issues within the DSM include the highly politicized nature of the processes of revision across various editions, various cultural, sexist, and gender biases inherent in specific diagnostic categories, and the relatively weak reliability and validity of the classification system.

One key question is whether the biomedical model is the most appropriate approach to psychological or mental conditions and their treatment. Some theorists have argued in favor of naturalistic accounts of disease, notably Thomas Szasz , , He thus argues that the concept of mental illness is a prescriptive concept used as though it were a merely descriptive one, and also a justificatory concept masquerading as an explanatory one.

However it is not always clear in his account what his evidence for these claims is, and in particular whether he is making an in principle objection or one that is grounded in the history of the mistreatment of people with mental illnesses, and the disservice done to them in part because of the adoption of the medical model.

In addition, some have noted that some psychiatric conditions do in fact correlate with physiologically detectable and other types of biological abnormalities. For instance twin studies have demonstrated that genetics is a major factor in the etiology of schizophrenia among other conditions typically considered to be psychiatric, although clearly not all conditions that are diagnosable according to contemporary psychiatric standards fit this model.

Other authors, notably George Engel , have argued for the need to unify our understandings of mental and physical illness under a broader, biopsychosocial model. Such a model would focus clinicians to take account of both the physical, psychological, and social factors that contribute to ill health, in contrast to the traditional biomedical model which is faulted for being overly reductionistic rather than holistic. Such an account, it is claimed, would be more effective in dealing with borderline cases including people who are told they are in need of treatment due to abnormal lab results or similar but who are feeling well, as well as those who appear to have no underlying somatic disease condition but are feeling unwell.

Hence this type of account does not draw any sharp distinction between the physical and the mental or even the social , leaving the question of appropriate therapies or approaches as a matter to be decided by the doctor and his or her patient.

Engel compellingly defended this type of account as more appropriate not only for clinical work but for research and teaching in medicine. It is arguable that it has implicitly and often explicitly been adopted in much of current-day medical practice and teaching, although it is less clear whether it has had much influence in biomedical research, much of which tends to remain more reductionistic in its nature.

There is no widely accepted notion of what a scientific theory is. The logical positivists thought that theories are sets of propositions, formalizable in first-order logic, at one point, and as classes of set-theoretic models at another. For our purposes here one can distinguish two senses of theory, a narrower and a broader sense. In the narrower sense, a theory comprises a set of symbols and concepts used to represent the entities in a domain of discourse as well as a set of simple general-purpose principles that describe the behavior of these entities in abstract terms.

In the broader sense, theory refers to any statement or set of statements used to explain the phenomena of a given domain. In medicine one can find theories in both the narrower and the broader sense. The humors are in balance in a healthy person; diseases are explained by excesses or deficiencies in one or more humors. Humorism has ancient origins and influenced Western medicine well into the 18th century.

Eastern medicine has analogous systems of thought. Indian Ayurveda medicine, for example, is a theory of the three primary humors wind, bile, and phlegm, and diseases are similarly understood as imbalances in humors Magner In contemporary Western medicine, such highly unifying and general theories play a limited role, however.

Contemporary Western medical researchers and practitioners instead seek to explain medical outcomes using mechanistic hypotheses about their causes—symptoms by hypotheses about diseases, diseases by hypotheses about antecedents, epidemics by hypotheses about changes in environmental or behavioral conditions Thagard What distinguishes these contemporary medical theories from the ancient approaches is that the causes of symptoms, diseases, and epidemics can in principle be as multifarious as the outcomes themselves; in the ancient approaches, lack of humoral balance was the only possible cause.

In contemporary Western medicine, there is no presupposition concerning number, form, or mode of action of the causes that explain the outcome other than there being some cause or set of causes responsible. Not every cause is equally explanatory. The lung cancer may have had a genetic mutation, the deposition of carcinogens in lung tissue and smoking in its causal history. There is no absolute answer to this question. The goodness of a medical explanation depends in part on the context in which it is given see entry on scientific explanation.

The adequacy of a medical explanation is related to our ability to intervene on the factor in question. A pulmonary embolism can be prevented by screening the patient for blood clots. The accumulation of carcinogens in lung tissue can be prevented by stopping smoking. By contrast, even though certain kinds of genetic mutations are in the causal history of any cancer, the mutation is not at present of much explanatory interest to most clinicians, as this is not a factor on which they can easily intervene.

There is considerable current medical research to identify mutations associated with various subtypes of cancer and using these to develop targeted therapies and interventions, as well as to provide more accurate prognostic information. Medical explanation, thus, is closely related to our instrumental interests in controlling, preventing and controlling outcomes Whitbeck One issue that is currently debated in the philosophy of medicine is the desirability or lack thereof of citing information about the mechanisms responsible for a medical outcome to explain this outcome.

While mechanisms are usually characterized in causal terms e. Absences, such as lack of sunlight, can cause medical outcomes but are not related to them through continuous mechanisms from cause to effect Reiss Neuroscientific explanations are often acceptable despite the lack of knowledge or false assumptions about mechanisms Weber However, we may ask whether mechanistic explanations are generally preferable to non-mechanistic causal explanations.

Many medical researchers and philosophers of medicine subscribe to a reductionist paradigm, according to which bottom-up explanations that focus on the generative physiological mechanisms for medical outcomes are the only acceptable ones or at least always preferable. To prevent these consequences, is it necessary to stop smoking?

Is it possible to produce cigarettes the smoking of which has fewer or no adverse consequences? What is the best policy to improve morbidity and mortality from lung cancer? Knowing that it is specific carcinogens in tobacco smoke and genetic susceptibility that are jointly responsible for the onset of the disease helps to address many of these questions. Nevertheless it would be wrong to assume that we cannot explain outcomes without full knowledge of the mechanisms responsible.

When, in the mids, smoking was established as a cause of lung cancer, it was certainly possible to explain lung cancer epidemics in many countries where people had exchanged pipe smoking for cigarette smoking half a century earlier—even though the mechanism of action was not understood at the time.

Differences in lung cancer incidence between men and women or between different countries can be explained with reference to different smoking behaviors. Policy interventions, in this case the addition of warning labels to cigarette packets, could not wait until sufficient mechanistic knowledge was available, nor did they have to wait. If nothing else, this is certainly a position that is consistent with the explanatory practices in the field.

As in many fields, debates over reductionism versus holism are rife in medicine both with reference to medical research and practice, and the terms often are used rather loosely to mean a range of things for a related discussion see entry on reductionism in biology. In the broadest terms, reductionistic approaches to disease look for fundamental mechanisms or processes that are the underlying causes of that disease.

In recent years in light of large-scale genomic sequencing initiatives notably the Human Genome Project, there has been considerable emphasis on reducing diseases to the genetic or molecular level. As Catherine Dekeuwer notes, given that there probably is genetic variation in susceptibility to virtually all diseases, there is no clear demarcation between genetic diseases and diseases for which there are genetic risk factors; hence she argues that our tendency to focus on genetic determinants of disease may reinforce folk notions of the geneticization of both people and of human behavior.

With regard to research, critics of reductionism point out that there has been an overemphasis on the pursuit of genetic or molecular level explanations of disease to the neglect of alternative levels of explanation. Further, such limitations are highly detrimental to patients, especially because there are not likely to be short-term cures or treatments for most genetic diseases, perhaps beyond avoiding having children carrying particular genes in the first instance see for instance Hubbard and Wald , although this domain of medicine is rapidly changing as new treatments are developed and the understandings of the effects of genomic mutations improve.

In addition, as Elisabeth Lloyd argues, higher levels of social organization that are culturally sanctioned have unrecognized causal effects on health, and hence medical research should not be restricted solely to the molecular level. Kelly Smith disputes this, noting that the second condition depends on an extremely problematic distinction between causes in this case genes and mere conditions e.

More recently it has been argued that although explanatory reduction cannot be defended on metaphysical grounds, reductive explanations might be indispensable ways to address certain questions in the most accurate, adequate, and efficient ways van Bouwel et al.

According to a widely cited definition:. Evidence based medicine is the conscientious, explicit, and judicious use of current best evidence in making decisions about the care of individual patients. Sackett et al. Such a definition has bite only when the concept of evidence used is relatively narrow. While there is no universally accepted hierarchy, the different proposed hierarchies all agree in the priority they give to randomized controlled trials RCTs and reviews thereof.

A typical hierarchy looks as follows Weightman et al. Individual patients are assigned to a group by means of a random process such as the flip of a coin. A placebo is an intervention that resembles the new treatment in all respects except that it has no known ingredients active for the condition under investigation i. Patients, researchers, nurses, and analysts are all blinded with respect to treatment status of all patients until after the analysis.

After a period of time, a pre-determined outcome variable is observed and the values of the variable are compared between the groups. If the value of the outcome variable differs between different treatment groups at the desired level of statistical significance, the treatment is judged to be effective. Proponents of EBM regard RCTs as reliable means to judge treatment efficacy because they can help to control for a variety of though not all biases and confounders.

If, for instance, the symptoms of a patient or group of patients improve after an intervention, this may be due to spontaneous remission rather than the treatment. Similarly, a design in which the allocation to treatment and control groups is done by a non-random process, it is possible that healthier patients end up in the treatment and less healthy patients in the control group.

If so, the measured improvement may be due to the health status of the patients rather than the intervention. Especially if the allocation is done by a medical researcher who has a stake in the matter for instance because she has developed the new treatment , allocation decisions may consciously or subconsciously be influenced by expectations about who will profit from the intervention and thus create unbalanced groups.

Allocation by a random process helps to control this source of bias. No one denies that RCTs are powerful experimental designs—and that their power stems from the ability to control numerous sources of bias and confounding. Specifically, one may be led to assume that RCTs are necessary for reliable causal inference or that RCTs are guaranteed to deliver reliable results. A number of philosophers of medicine have in the past decade or so argued that these stronger claims do not hold to scrutiny.

A final but very important issue is that of the external validity of the RCT results. Even under ideal conditions i. Typical test populations differ from the target populations i. For example, many RCTs will exclude elderly patients or patients with co-morbidities but the treatment will be marketed to these patients.

For financial reasons, many RCTs are nowadays conducted in developing countries whereas the treatments are mainly or exclusively marketed to patients in developed countries. Whereas the protocols for conducting an RCT are very strict and detailed, there are no good guidelines how to make treatment decisions when the patient at hand belongs to a population that differs from the population in which the RCT was conducted e.

There are in fact two problems of external validity in the application of RCT results. On the one hand there is the population-level problem of making an inference from test to target population. On the other hand, there is the problem of making an inference from population to individual. For this claim to be true, the treatment must be on average effective, which allows the effectiveness to vary among the individuals in the population.

Indeed, it is possible that the intervention is effective and beneficial on average but ineffective or positively harmful in some individuals i. Evidence-Based Medicine Working Group John Worrall argues that, at the end of the day, RCTs are a powerful means to control selection bias, but no more than that Worrall , a,b.

As he uses the term, selection bias occurs when treatment and control group are unbalanced with respect to some prognostic factors because a medical researcher has selected which patients will receive which treatment. Selection bias in this sense obviously cannot occur in an RCT because in an RCT the allocation is made by a random process.

But it is also clear that randomization is at best sufficient but not necessary to achieve the result. A large number of alternative designs may be used to the same effect: allocation can be made by a strict, albeit non-random protocol; allocation is made by non-experts who are unrelated to the treatment development and therefore have no expectations concerning outcomes; treatment and control groups are deliberately matched again by persons who have nothing at stake or according to some protocol ; and so on.

A controversial issue is the role of mechanistic knowledge, that is, knowledge about the biological and physiological mechanisms responsible for medical outcomes and thus treatment efficacy should play in EBM. Nevertheless, a number of philosophers of medicine have pointed out that mechanistic knowledge is in fact important in EBM or that it should receive more attention. Federica Russo and Jon Williamson have, for instance, argued that causal claims need both statistical evidence as well as evidence about the mechanisms that connect an intervention with the outcome variable in order to be established Russo and Williamson Others disagree Reiss or qualify the claim Gillies ; Howick a; Illari Further, it has been pointed out that mechanistic knowledge plays an important role in the design and preparation of an RCT, as well as in the interpretation and application of RCT results La Caze ; Solomon Especially when it comes to extrapolating research results from a test to another population, mechanistic knowledge is supposed to be vital Steel ; see also next section.

On the other hand, knowledge about mechanisms is often highly problematic and should not be relied on too heavily in applications Andersen New therapies are often trialed using animal models before they are tested on humans in a randomized trial. Animal models also play important roles in establishing whether or not a substance is toxic for humans. The International Agency for Research on Cancer IARC , for example, classifies substances with respect to the quality of the evidence for their carcinogenicity into five groups.

Evidence from animal models is referred to in the characterization of each group IARC This raises questions about how such extrapolations from animal models to humans work, and how reliable they are. Animal models are widely used in biomedical research because experimental interventions on animals are easier to conduct and cheaper than experiments on humans.

Both kinds of experiments involve ethical dilemmas, but animal experimentation is usually regarded as less problematic from an ethical point of view than experimentation with humans. At any rate, the number of animals killed, maimed, or made sick in biomedical research is much higher than the number of humans adversely affected in this research.

The problem is essentially this. What is true of a model can be presumed to be true of the target only to the extent that the model is similar to the target in relevant respects. The reason we experiment on models in the first place is, however, that the model differs in important respects from the target if animals were just like humans, we would not find experiments on the former to be ethically less problematic than experiments on the latter.

Extrapolation—the inference from model to target—is therefore only worthwhile to the extent that there are significant limitations in our ability to study the target directly. If so, there can be no good grounds to decide whether a model is a good one for the target.

To do so, we would have to investigate whether the target is relevantly similar to the model; but if we could do so, there would be no reason to study the model in the first place. This inferential problem has led some commentators to maintain highly skeptical views concerning our ability to use animals as models for humans in biomedical research.

Hugh LaFollette and Niall Shanks argue that animal models cannot be reliably used for extrapolation at all, but at best only heuristically, as sources of hypotheses that have to be tested on humans LaFollette and Shanks The former can be used to make reliable predictions about target populations of interest; the latter only heuristically.

The main premise in their argument that animal models in biomedical research are at best HAMs but not CAMs is that for a model to be a CAM there cannot be causally relevant disanalogies between model and target—a condition which is rarely if ever met by animal models again, this is why we study animals in the laboratory in the first place.

Daniel Steel ch. Whether a claim about a model can be extrapolated depends, he argues, also on the strength of the claim to be exported. It is one thing, say, to reason from. He assumes that causes C such as medical interventions or the ingestion of toxic substances bring about their effects E such as the appearance of symptoms or improvements or deteriorations of symptoms through a series of steps or stages.

To trace a causal process means to investigate through what set of stages C brings about E. Process tracing is comparative when the set of stages through which C brings about E in one species or population is compared to the set through which it does so if it does so indeed in another. Comparative process tracing would be futile if, in order to know that C causes E in the target species or population, we would have to compare all the stages of the process between model and target.

This is because in order to do so, we would have to know all stages of the process through which C causes E , but if we did, we would already know that C causes E. Thus, if we compare an intermediate stage of the process which obtains in the model with that stage in the target and find them to be relevantly similar, then the only differences that may still obtain will be downstream from this stage.

How useful comparative process tracing is as a method for extrapolation for the biomedical sciences depends on how reliable the assumption that only downstream differences matter to extrapolation is, the reliability with which stages where there might be differences between model and target can be identified and the reliability of our mechanistic knowledge more generally. If, say, our reasons for supposing that C causes E through a series of stages X , Y , Z in the model, or that X and Z are the stages where model and target are likely to differ, are not very strong, then the method does not get off the ground.

This is an issue that depends on the quality of the existing knowledge about a given case and cannot be addressed for the biomedical sciences as a whole. There are certainly some examples of well-established causal claims where it is known only that C causes E but the details of the causal process are entirely beyond our current grasp Reiss forthcoming-a.

An alternative to comparative process tracing that has been proposed is extrapolation by knowledge of causal capacities. If C has a causal capacity to bring about E , then C causes E in a somewhat stable or invariant manner.

Specifically, C will then continue to contribute to the production of E even when disturbing factors are present Cartwright And therefore, if C causes E in a model species or population and C has the causal capacity to bring about E , then there is some reason to believe that C causes E also in the target species or population for a defense, see Cartwright The usefulness of the method of extrapolation by causal capacities depends, among other things, on the extent to which biomedical factors can be characterized as having capacities.

Many biomedical causes do indeed have some degree of stability. These figures suggest a reading along the lines of,. Reiss b: But there is a high degree of interaction with other factors as well. Whether or not a substance is toxic for an organism depends on minute details of its metabolic system, and unless the conditions are just right, the organism may not be affected by the substance at all. To what extent this method will be successful therefore similarly case-dependent as comparative process tracing.

As we can see, there is no general answer to the question whether or not animal studies are valuable from a purely epistemic as opposed to ethical, economic, or combined view. Other authors have developed a practice-based taxonomy of animal modes to allow more accurate assessment of the epistemic merits and shortcomings, and predictive capacities of specific modeling practices Degeling and Johnson There is much evidence that species differ enormously with respect to their susceptibility to have toxic reactions to substances.

Thus, while it is very likely that for any one toxin, there is some species that is predictive of the human response, it is often hard to tell which one is most appropriate for any particular toxin. A species that predicts the human response well for one substance may be a bad model for another.

However, some authors suggest that extrapolations from animal models have been made successfully in at least some cases Steel discusses the extrapolation of claims concerning the carcinogenicity of aflatoxin from Fisher rats to humans; see Reiss a for a critical appraisal and Steel for a response. Frequently, in the biomedical sciences, reliable animal or other non-human models are not available and RCTs on humans are infeasible for ethical or practical reasons. In these and other cases, biomedical hypotheses can be established using observational methods.

As we have seen in Section 5 , evidence-based medicine regards observational methods as generally less reliable than RCTs and other experimental methods. This is because observational studies are subject to a host of confounders and biases that can be controlled when the hypothesis is tested by a—well-designed and well-conducted—RCT.

But it is not the case that observational methods cannot deliver reliable results. In fact, it is well possible that the medical knowledge that has been established observationally by far exceeds the knowledge that comes from RCTs. Here are some examples of medical interventions that are widely accepted as effective but whose effectiveness has not been tested using RCTs: penicillin in the treatment of pneumonia, aspirin for mild headache, diuretics for heart failure, appendectomy for acute appendicitis and cholecystectomy for gallstone disease Worrall a: ; automatic external defibrillation to start a stopped heart, tracheostomy to open a blocked air passage, the Heimlich maneuver to dislodge an obstruction in the breathing passages, rabies vaccines and epinephrine in the treatment of anaphylactic shock Howick b, Observational studies often begin by reporting a recorded correlation between a medical outcome of interest and one or a set of independent variables: lung cancer rates are higher in groups of smokers than in groups of non-smokers, liver cancer rates are higher in populations that tend to consume food that has been contaminated with aflatoxin than in populations whose food is uncontaminated, to give a few examples.

That smoking causes lung cancer, or aflatoxin cancer of the liver, would indeed account for the observed correlations. But so would a variety of other hypotheses. Ronald Fisher famously proposed that it may be the case that early stages of bronchial carcinoma cause an individual to crave cigarettes, and he provided some evidence that both smoking behavior and susceptibility to lung cancer have a common genetic basis Fisher Selection bias is normally understood as the bias that obtains when individuals self-select into the observed population and the reasons for which they do so are correlated with the outcome variable.

If an observational study examines only hospitalized patients and smokers are more likely to be in hospital for reasons that have nothing to do with lung cancer, then smoking and lung cancer can be correlated in the data even if the variables are independent in the general population. Mismeasurement and diagnostic error provide another account of spurious correlation.

Suppose tuberculosis was on the rise a generation or so after many people traded pipe smoking for cigarette smoking. Then, if it was difficult to distinguish a death from tuberculosis from a death from lung cancer because necropsy techniques were not sufficiently well developed, the data might again show a correlation even though the population variables are uncorrelated. Retrospective observational studies work by ruling out alternative hypotheses such as these ex post rather than controlling for them ex ante as RCTs do Reiss a.

In an RCT, mismeasurement should not obtain because the protocol specifies measurement procedures for the outcome variables in great detail in advance. Selection bias should not obtain because patients are randomized into treatment groups.

Once allocated to a group, they are prevented from obtaining another treatment elsewhere, and researchers make sure that patients comply with the treatment regime. But there are equivalent means to rule out these possibilities in observational settings. While it may well be the case that early stages of cancer cause a craving for cigarettes, this hypothesis cannot explain the protective effect that stopping smoking has. However, it could be shown that in order to account for the observed rise in lung cancer incidence, the diagnostic error at autopsy among older people would have to have been an order of magnitude higher than the diagnostic error among younger people Gilliam Mismeasurement could therefore also be ruled out.

Similar considerations helped to rule out other alternative hypotheses Cornfield et al. Even if one were to believe, with the proponents of EBM, that observational studies are generally less reliable than RCTs, medicine could—fairly obviously—not do without them. There are large numbers of pressing questions that could not be addressed by an RCT for ethical, financial and other practical reasons.

This is not merely because of the straightforward ethical issues involved in deliberately exposing humans to a potential carcinogen for the sake of medical progress. It is also because exposure to low levels of aflatoxin can take many years or even decades to produce symptoms. The ability of researchers to control food intake in a large group of experimental subjects for a very long has evident practical financial and limitations. Moreover, it is not clear that RCTs are always more reliable than observational studies to answer questions both methods are able to address.

Whether or not a study is reliable depends on whether or not confounders and biases have in fact been eliminated, not by which method they have been eliminated. Issues concerning the reliability of a method can be entangled with issues concerning its ability to address the research question the biomedical scientist seeks to answer. Both RCTs and observational studies in the biomedical sciences are typically employed to test rather complex hypotheses about the safety and efficacy of medical interventions.

It may well be that some of the issues are more reliably treated by one method and others by the other. A famous controversy in which the results from observational studies and those from RCTs conflicted was that over the benefits and safety of hormone replacement therapy HRT in the early s Vandenbroucke HRT seemed protective for coronary heart disease in observational studies, whereas RCTs indicated an increase in the first years of use.

For breast cancer, combined hormone preparations showed a smaller risk in an RCT than in observational studies. In the end it turned out that the timescale of the effects was responsible, and that because of the way they are typically run, observational studies got some issues right and RCTs others:. The observational studies had picked up a true signal for the women closer to menopause. In the randomised trial, that signal was diluted because fewer women close to menopause were enrolled… The randomised trials had it right for coronary heart disease but failed to sufficiently focus on women close to menopause for breast cancer.

The main reasons for the discrepancies were changes of the effects of HRT over different times… Vandenbroucke Case reports remain extremely popular in medicine both as publications to communicate within the field and for pedagogical purposes. In short, a case report describes a medical problem experienced by one or more patient, usually involving the presentation of an illness or similar that in some way difficult to explain or categorize based on existing understandings of disease or understandings of physiology and pathology.

Cases in medicine take highly standardized forms of presentation which are inculcated in health care professionals during their education, and many have commented on their highly standardized narrative structure and its epistemic and other implications Hunter ; Hurwitz Cases typically provide details on the presentation of the disease, diagnosis, treatment, and outcomes for the patient, with a focus on practice-based observations and clinical care rather than the results of randomized controlled trials or other experimental methodologies.

One of the purposes of cases is to gather detailed information including facts that may not be immediately relevant, but that could prove to be Ankeny Thus the information contained in the case and the case itself can be useful over the long term particularly if it can be systematically combined with other cases into larger datasets. Single cases are seen by some as problematic as a form of evidence particularly in the era of EBM, because they often focus on highly unusual manifestations of illness and disease, rather than typical or repeatedly observed conditions that might support generalizable rules.

However standard accounts of EBM include the case series as a type of evidence, which involves the aggregation of individual cases of patients with similar attributes e. EBM does place the case series quite low in its hierarchy of evidence but nonetheless it is acknowledged that cases have potential usefulness especially where forms of evidence that rate more highly are not available, as may often be the case where human patients are concerned due to practical or ethical reasons, or where the available evidence at higher levels has been produced in a manner that is methodologically or otherwise flawed.

Cases can serve other purposes: for instance analyses of cases can provide working hypotheses about casual attribution that can ground further tests of causal relations Ankeny , which in turn allows use of more traditional methodologies such as RCTs, cohort studies, and so on to explore these causal hypotheses. In the context of clinical care, cases can allow health care providers to identify a cause that can be manipulated to cure or prevent the condition in question, in order to treat ill patients, even in the absence of more rigorous forms of evidence.

Diagnosis is the process through which a clinician determines what is wrong with a patient who is ill or ailing in some way. Although a critical part of the practice of medicine, it has been relatively neglected in the literature of philosophy of medicine particularly in comparison to more statistically-based methods for evaluating evidence in other fields Stanley and Campos The key philosophical issues that arise in this context relate to how such determinations can be made in a manner that is accurate given the high amount of uncertainty and complexity often associated with the human condition, and hence involve logical, epistemological, and ontological issues.

The usual way of proceeding in a clinical setting is to ask the patient to articulate what is ailing him or her, and thus to use a standardized reporting format to detail various symptoms which represent subjective manifestations of the illness or disease. In addition, clinicians perform various tests and examinations that allow more objective manifestations or signs to be recorded, such as heart rate, blood pressure and count, reflexes, and so on.

A perennial debate in the philosophy of medicine is what constitutes symptoms and signs and whether they are in fact distinct, which relates to deeper issues about the realism of disease conditions as discussed above Section 2.

The tricky part of the process is to find a means for mapping these symptoms and signs onto a particular disease condition. Some would advocate that this process is no different than usual methods in philosophy of science for hypothesis generation and testing based on evidence, and this type of model fits with what is termed differential diagnosis.

Differential diagnosis involves a set of hypothetical explanations for a particular condition which come to be ruled in or out based on the evidence together with additional data that is collected, hence relying on a form of reasoning via decision nodes or algorithmic pathways Stanley and Campos However this approach can be dangerous particularly among novices, given the large number of similar patterns among common diseases.

Some have claimed that the making of a diagnosis is both a deontic act and computable, and that diagnoses are relative only inasmuch as they occur in a complex context which in turn makes them a social practice Sadegh-Zadeh Computer-assisted diagnostic techniques have improved and are used increasingly in clinical settings; Kenneth Schaffner provided an early analysis of the criteria which an ideal diagnostic logic would need to satisfy for updated discussions see Schaffner , , and for arguments about the limitations of such types of diagnosis see Wartofsky The epidemiologists who initiated the formal EBM movement in the early s had good reason to be skeptical about expert opinion.

When therapies are subjected to systematic tests, tradition and expert opinion are sometimes shown to be flawed. John Worrall discusses three examples: grommets for glue ear, ventricular ectopic beats repressing substances such as encainide or flecainide for cardiac arrest, and routine fetal heart rate monitoring to prevent infant death Worrall a: In each case we have a procedure the effectiveness of which is indicated by common sense and knowledge about the patho-physiological pathways—glue ear is a condition produced by a build-up of fluid in the middle ear that is unable to drain away because of pressure differentials, grommets act by letting air into the middle ear and thereby equalizing pressure, for instance—but which, when tested by a randomized trial, turns out to be ineffective at best and positively harmful in the worst case.

Misjudgments concerning the efficacy of therapies for purely epistemic reasons are not the only worry that one might have about expert opinion. Medical experts and patients are in what economists call a principal-agent relationship. The principal—in this case, the patient—desires the delivery of a certain good or service—in this case, his health. He instructs an agent—in this case, the doctor—with it, because he lacks the expertise to produce the good himself. The doctor may not always choose the optimal therapy for a patient we can suppose that it takes some effort to select the optimal therapy for a patient , and any therapy can be implemented sloppily.

Moreover, lacking expertise, the patient cannot observe the level of effort a doctor puts in. In our world, neither patients nor doctors are particularly rational, nor are they motivated purely by self-interest, there are ethical codes such as the modern form of the Hippocratic Oath, and the health sector is one of the most regulated industries of all. All this does not, however, change the incentive structure in which doctors and other providers of health services operate.

There is a further complication. Many, probably most, doctors have connections to the pharmaceutical industry in one form or another. Even if we suppose that doctors do not prescribe a therapy because they are paid to do so, marketing efforts directed at them will influence treatment recommendations, if only because they know certain pills better than others, or because some treatments are at the top of their heads.

For all these reasons, the EBM principle that treatment decisions should be based on the best available evidence from systematic research does not come out of nowhere. However, while these are all bad reasons to recommend Y over X in the light of the study result, there may be a variety of good reasons. As discussed in Section 5 , RCTs and many observational studies are population-level studies, which produce average results that are not straightforwardly applicable to individuals.

Instead, the RR may vary dramatically among the subpopulations of p , and it may well be the case that Y is more effective than X for some subpopulations. The same is true of side effects. Tonelli discusses a case where a patient who suffers from multiple sclerosis receives a treatment that does seem to alleviate her symptoms, but since she has started taking it, she has been plagued by severe episodes of depression. Clinical trial results indicate that the drug is effective in treating multiple sclerosis, and no adverse psychiatric effects have been reported.

Her GP and her psychiatrist now debate whether to continue the treatment. This is another reason why clinical judgment must be exercised in the derivation of a treatment recommendation. Unfortunately, experts—like all humans—are notoriously bad decision makers. Cognitive psychologists have established a large number of cognitive biases to which human experts are subject: they suffer from overconfidence e. Better numeracy and statistical training at universities can help to eliminate some cognitive biases Gigerenzer Computer-aided medical diagnosis and decision making may alleviate others.

No training or computer program can make normative judgments, however, and neither will help with adverse incentive structures and financial interests. These difficulties also beset committees of medical experts to which we are turning next. One way to help overcome expert bias is by making medical decisions not dependent on individual expert judgments but instead have groups of experts coming to some form of aggregate judgment.

The U. National Institutes of Health, for instance, used to organize so-called consensus conferences designed to resolve scientific controversy. Panel members are chosen from clinicians, researchers, methodologists and the general public. Federal employees are not eligible, nor are researchers who have published on the subject at hand or have financial conflicts of interest Solomon These exclusions are intended to contribute to controlling government influences as well as any biases due to financial or intellectual interests.

Consensus conferences and other mechanisms for reaching group judgments are clearly no panacea. More important in the present context is the observation that while these conferences possibly help to control some forms of partiality, they are ineffective in reducing others and may be responsible for the introduction of new biases. One concern is that panel members may read the existing evidence selectively, for instance, because of weighing salient studies or studies that are available to them more heavily.

Another is that phenomena such as groupthink Janis and peer pressure may influence results. In an NIH consensus conference panel members have to come to a verdict after only two days of hearings and deliberations. Under these conditions it is certainly possible that more outspoken panel members or those who perform well under extreme pressure have a undue influence on results.

Moreover, it is not clear that excluding clinicians who have published on the issue at hand is always such a good idea. After all, it is not implausible to maintain that those scientists who actively work on a research topic are those who best understand it and therefore can make the best informed judgments. For these and other reasons, Solomon , explores the consequences of judgment aggregation. In this process group members typically do not deliberate but instead cast their opinions which are then aggregated using some pre-determined procedure.

The majority rule would be a simple example of such a procedure. Coming to a group judgment using a mechanical procedure such as majority vote has a number of advantages. Under these conditions, then, a committee of experts is likely to make a better judgment than a single expert. Moreover, in the absence of deliberation and pressure to come to a unanimous results, and when voting is secret, the influence of groupthink, peer pressure etc.

When conditions a — c do not hold, results are more ambiguous or even negative. When experts are not reliable, i. When the outcome can have more than two values, inconsistent results can obtain. This can easily be demonstrated with an example in which there are three possible outcomes and three experts. Suppose, for instance, that a panel has to decide which of three treatments A , B and C is the most effective in treating some disease. The individual panel members have the following individual rankings:.

The majority rule is of course only one way to aggregate judgments. The Delphi method e. Experts answer questionnaires in several rounds. During this process the range of the estimate will often decrease, and it is hoped that the group will converge towards the correct answer. The process is stopped after a pre-determined stopping criterion such as number of rounds, achievement of consensus, stability of results, and an average of the estimates of the final round is used as result.

Solomon , raises a fundamental issue concerning group judgments that is entirely independent of the specific method used. She argues that we do not often find group judgment methods to determine the truth of scientific hypotheses or estimates of variables in the natural sciences though see Staley If there is uncertainty about, say, which of two alternative hypotheses is true or what value a natural constant has, scientists go out and test, experiment, measure.

Controversies, in other words, are settled on the basis of evidence, not individual or group opinion. Consequently, she recommends more widespread use of mechanical techniques for amalgamating evidence such as meta-analysis in lieu of consensus conferences and the like. The frequency of NIH consensus conferences has indeed markedly declined in recent years Solomon , But this is of course no reason to maintain that group judgments are no longer needed.

Consensus conferences may be the wrong tool for the purposes of the NIH, or the NIH may have a mistaken view about the ability of evidence to settle disputes adequately. Indeed, there are at least two reasons to believe that group judgment procedures are here to stay. The first reason is that, as we have seen above, medical decisions are always in part decisions about normative matters.

No treatment is entirely without side effects and so if judgments about efficacy are to be of practical guidance, they must include a weighing of benefit alleviation of disease symptoms against cost suffering from side effects —even if economic costs and benefits are not to be taken into consideration. Second, government agencies such as the U.

Food and Drug Administration FDA have to decide whether new treatments should be licensed to be marketed. These decisions often have significant consequences, and democracies tend to prefer to be able to hold someone accountable for making them.

Drug approval therefore cannot be determined on the basis of evidence according to some mechanical algorithm. Biddle discusses epistemological and moral issues of drug approval in the context of a case study on Vioxx, an analgesic. Vioxx was approved by the FDA in but five years later pulled from the market by its manufacturer Merck due to safety concerns.

It is estimated that some 55, people died from taking the drug Harris Biddle observes that the FDA is not sufficiently independent of the pharmaceutical industry to make unbiased decisions likely. To solve these problems of conflicts of interests, Biddle proposes to institute an adversarial system in which two groups of advocates, a group of representatives of the manufacturer and a group of independent scientists, would argue before a panel of judges over whether a drug should be allowed on the market.

The panel of judges in this model also consists of independent FDA or university scientists. He argues that the adversarial system would better acknowledge the fact that an increasing number of medical researchers have financial ties to the pharmaceutical industry by treating them as advocates rather than disinterested experts. See also Reiss and Wieten , Reiss forthcoming-b. There is no doubt that medical research is shaped by various external values, in ways similar to the value ladeness that is well-recognized in other areas of science see entry on scientific objectivity.

Many of these values create a variety of ethical dilemmas relating to equity of access to health care and similar. Even in recent years once medical research has been made more inclusive, this trend has introduced a host of additional philosophical and ethical issues Epstein For our purposes, we will focus on the implications of the systematic exclusion of certain types of individuals, groups, or diseases from research for future research as well as clinical medical practice in terms of the validity of evidence produced and decisions made based on that evidence.

In traditional medical research, it was generally assumed that white male participants could be used as the basis of generalizations that in turn could be extrapolated to all other populations, including minorities and females Dresser Reviews of the literature indicate that women in particular have been excluded especially older women , and that research on women has usually been related to reproductive function and capacity Inborn and Whittle Such types of research have been argued to fail the ideals of quality medical research as well as evidence-based health care Dodds Although some improvements have been made in recent years, there remain certain forms of blanket exclusions for instance of women of childbearing age or pregnant women in many types of medical research.

These types of systematic exclusion are highly problematic especially because there is clear evidence of critical differences between men and women with regard to a range of factors relating to receptivity to therapies for both biological and social reasons. In the case of minorities such as African-Americans in the United States, even when research trials seek to recruit them, a range of factors may contribute to them not being involved in medical and other types of research studies.

These include distrust due to historical and institutional racism including research performed without consent; lack of understanding about research and consent; social stigma; financial considerations; and lack of culturally-sensitive recruitment methods by researchers e.

Such gaps in medical research potentially lead to use of treatments or therapies that may in fact be harmful for particular groups, and may result in the withholding of therapies that might be beneficial. A final way in which our knowledge in medicine generated by research is potentially adversely affected by values is through the funding patterns connected with research. As implied above, pharmaceutical companies sponsor a considerable portion of drug trials and have a variety of interests at stake in these investments well beyond the gathering of evidence for the effectiveness or lack thereof of a particular product.

There is consistent evidence that negative research results typically are suppressed when sponsored by industry Lexchin a , leading to a bias in what is reported and thus what evidence is available on which to make prescribing and treatment decisions. Bias also has been found in a number of other areas: within the study itself in the choice of research question or topic of investigation, in the choice of doses or drugs against which the drug under study is to be compared, in the control over trial design and various changes in protocols, and in decisions to terminate clinical trials early, and in the reinterpretation of data, as well as in the publication of data such as restrictions on publication rights, use of fake journals, favoring journal supplements and symposia rather than peer review venues, the use of ghostwriting, and in the details of the reporting of results and outcomes Sismondo ; Reiss b; Lexchin b.

All of these issues weaken the evidence base on which clinical care judgments are made, and also lead to potentially adverse effects for patients. In order to evaluate medical outcomes quantitatively, they have to be measured. There are numerous reasons for aiming to quantify medical outcomes. We may want to compare two or more treatments with respect to their efficacy at relieving certain symptoms or their ability to prevent deaths due to a certain disease.

When resources are scarce, we may not only want to invest in treatments that are efficacious that is, they do improve patient morbidity, mortality or both but also efficient that is, it is more efficacious than other treatments relative to the cost of procuring it.

For matters of international comparison, development and international justice, we also want to have measures of disease burden : Which of a number of tropical diseases has the highest cost in terms of increased morbidity and mortality? For each research dollar spent on treatments for disease X , how much can we expect to reduce the morbidity and mortality it causes? Clinical trials now often report so-called patient-reported outcome measures or PROMs.

It might ask, for example, how difficult patients find it to climb up a flight of stairs after hip surgery or whether or not a cancer treatment helps them to pursue their hobbies. The main goal of a PROM is the assessment of treatment benefit or risk in cases where the medical outcome is best known by the patient or best measured from the patient perspective. PROMs can vary considerably in length and complexity depending on the concept that is being measured.

In simple, straightforward cases e. In others, it may be necessary to address several aspects of a more complex functioning with a number of questions each.

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