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Beyond ‘evidence’. Commentary on Tonelli (2006), Integrating evidence into clinical practice: an alternative to evidence‐based approaches. <i> Journal of Evaluation in Clinical Practice</i> 12, 248–256

2006· letter· en· W2036284976 on OpenAlexaffabout
Manoj Kumar Gupta

Bibliographic record

VenueJournal of Evaluation in Clinical Practice · 2006
Typeletter
Languageen
FieldMedicine
TopicClinical Reasoning and Diagnostic Skills
Canadian institutionsMcMaster University
Fundersnot available
KeywordsEmpirical evidenceClinical PracticeEvidence-based medicineFoundation (evidence)Experiential knowledgeMedicineEpistemologyAlternative medicinePsychologyKnowledge managementComputer scienceFamily medicinePolitical sciencePhilosophyPathology

Abstract

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In his excellent article, ‘Integrating evidence into clinical practice: an alternative to evidence-based approaches’, Tonelli tackles the very important question of what constitutes ‘integration’, a term found in the definition of evidence-based medicine (EBM), that is, ‘EBM is the integration of best research evidence with clinical expertise and patient values’. Although this definition serves as the foundation for the entire EBM project, EBM’s proponents have paid very little scholarly attention to analysing its various component terms. Furthermore, as Tonelli rightly points out, EBM assumes a great deal, particularly about the nature of knowledge. These assumptions create certain conceptual problems. One such problem according to Tonelli, is that EBM treats all sources of information as similar in kind, rendering them directly comparable. This allows EBM to rank order various sources of information. Tonelli argues that sources of information are not all similar in kind and therefore not comparable. He proposes five different ‘topics’ which comprise the factors relevant to clinical decision making: empirical evidence, experiential evidence, pathophysiologic rationale, patient values and preferences, and system features. In his view one cannot, for example, consider research data to be a superior form of knowledge to patient values. These kinds of knowledge are entirely different and must be considered on their own terms. Their weight will vary from case to case depending on the particular details of any given clinical situation. The implication of this view for clinical practice is that different types of knowledge cannot be prioritized in terms of their importance in all circumstances but rather, the relative contribution of each piece of knowledge must be evaluated in the context of an entire clinical scenario. This process is the essence of casuistic reasoning. Tonelli puts forward casuistry as a model of clinical decision making in response to EBM’s deficits as a decision-making model, particularly its failure to explain what it means by ‘integration.’ EBM really only addresses the component of medical decision making concerned with the effectiveness of interventions even though many other factors are required to make an actual decision. Through his discussion of casuistry, Tonelli argues cogently for a richer and more textured notion of clinical decision making than what EBM currently provides. EBM has lacked clarity concerning whether it is a model of clinical decision making, and if so, how it is supposed to work. Tonelli’s contribution is therefore apposite and timely. While one of the authoritative texts on EBM, the Users’ Guide to the Medical Literature (Guyatt & Rennie 2002), states that EBM is ‘about clinical decision making’, it alternately states that EBM is ‘about solving clinical problems’. In a longer statement of purpose, the Users’ Guide states that ‘. . . the goal is to be aware of the evidence on which one’s practice is based, the soundness of the evidence, and the strength of inference that evidence permits.’ Another authoritative source, Evidence-Based Medicine: How to Practice and Teach EBM (Sackett et al. 2000), reveals a different view. Here EBM is described as a ‘philosophy of medical practice based on knowledge and understanding of the medical literature supporting each clinical decision’ and later as a practice whose goal is forming ‘a diagnostic and therapeutic alliance’ between doctor and patient as a means of ‘optimizing clinical outcomes and quality of life.’ While these descriptions of the purpose of EBM vary considerably, they all seem to imply, at the very least, that EBM is supposed to have something to do with making decisions in a clinical context. If EBM is a model of clinical decision making, it is insufficiently detailed in its discussion of basic issues such as what ‘integration’ actually entails. Where I differ with Tonelli’s analysis is in his classification of empirical data and experience as forms of ‘evidence’ or evidentiary warrants for action while pathophysiologic rationale, patient values and preferences, and systems issues are non-evidentiary warrants for action. This division of the five topics into evidentiary and non-evidentiary sources can have the effect of replicating the very hierarchical ordering that Tonelli is trying to avoid. Because EBM is focused on finding and applying ‘evidence’, however, defined, identifying something as ‘non-evidentiary’ suggests from the outset that it is less valuable than those bits of information considered ‘evidentiary.’ This tendency reflects EBM’s under analysis of the concept of evidence itself. EBM supplies only a basic discussion of evidence which reveals that evidence is the same thing as data from clinical research studies, and that this form of information is superior to other forms, which are implied to be ‘non-evidence.’ A conceptual analysis of evidence is a complex topic to which many excellent scholars have devoted extensive discussion. My aim is neither to summarize nor do justice to that body of work here. Indeed, some authors have concluded that the concept of ‘evidence’ may be resistant to a single analysis applicable to all circumstances in which we use the term (Schum 1994, p. 16). Nevertheless, there are some general features of evidence that are worth recalling when considering the different types of medical knowledge and when thinking about what the label ‘evidence-based’ actually means in clinical decision making. We use several different terms to describe the basis of our clinical decisions, for example, data, information, intuition, judgement, knowledge and evidence. There are differences in what these terms mean, and these can revealed by the context in which they are used. For example, a numerical result from an randomized control trial may be a piece of data, but it may not be informative. Or, a patient may reveal a piece of information about herself, such as which store she was in when a symptom started, but this may not increase the doctor’s knowledge about her. What we mean by evidence has both general and context-specific features. For a piece of information or data to be considered evidence, it must increase the likelihood of an inference being true. In other words, evidence must be evidence for something. Other general features of the concept of evidence are that it must be based on information emerging from a credible source and be relevant to the inference at hand (Schum 1994, pp. 17–20). Can we state definitively, as Tonelli does, that there are some factors that will always, or never, be evidentiary in the context of decision making about individual patients’ care? Clinical research data about a treatment for a certain condition may always be considered to be potentially evidentiary when making decisions about whether to recommend that treatment. However, any particular data set may be sufficiently flawed to be lacking in credibility or unlikely to increase the probability of some inference being true. In this case, we could not call those research data ‘evidence’, because they fail a basic test of what constitutes evidence: they do not have the capability of supporting an inference. On the other hand, are there pieces of information that are non-evidentiary under any circumstances? What about patient values? How can someone’s belief that something is morally right constitute evidence? When the definition of EBM mentions integrating ‘patient values’, it is writing from the point of view of practitioners. That is, the notion of ‘patient values’ refers to the fact of people having values, not the actual values themselves. The value itself may be non-evidentiary, but the fact that someone holds a certain value may very well be evidentiary in certain cases. For example, a patient might hold a moral value that no one should have treatment forcibly administered, including for severe mental illness, because bodily integrity is a basic good. A practitioner may know that this particular person holds this value so strongly that she or he will fight any form of forcible medication in hospital and will refuse the treatment when out of hospital. This patient’s values about forcible treatment are certainly not evidence for an inference about the effectiveness of the treatment one may wish to offer. But, the fact of him or her having this value is evidence about an inference about whether she or he might want the treatment in question. Let us further imagine there is a law in place that allows forcible treatment under certain circumstances. Again, this is not evidence for an inference that the treatment works, but it is evidence for an inference about what society’s values are in such cases. These and many other inferences will have to be considered in order to make a clinical recommendation in this scenario. Here is where Tonelli’s use of casuistry as a description of clinical decision making is apt. Casuistry allows us to weigh up different inferences and facts, and consider the weight of each in light of the particular circumstances of each case. We are not required to prejudge the value of any piece of knowledge until the context of the scenario is understood. Research data about effectiveness of treatments can be understood in a more balanced way – not as a definitive or singular guide to action, but one of many factors that must be considered when making a clinical recommendation. If casuistry is a good model for clinical decision making, does it matter if we classify certain inputs as evidentiary and others as non-evidentiary? Inasmuch as EBM states that we ought to base our practice upon ‘evidence’, it is important to understand what evidence is. EBM has provided little analysis of this concept, nor has it sufficiently defended its claim about the inherent superiority of certain kinds of empirical research data in clinical decision making. As such, it risks becoming as authoritarian as the practices it eschews. Stripping certain types of information of the privileged label of ‘evidence’ requires us to consider the relevance and correctness of each piece of knowledge on its own terms rather than borrowing from the perceived credibility of ‘evidence’ and EBM. Beginning with our various sources of information on an equal footing also reminds us of the fallibilism of our knowledge, including what we ultimately deem to be ‘evidentiary’ (Upshur 2000, p. 96). Forcing all information in clinical decision making to be scrutinized more closely meets the legitimate challenge EBM has offered: to be explicit in justifying our medical recommendations. We may never be able to justify every decision, empirically or otherwise, but we should try to make explicit the process through which clinical decision making occurs. The author wishes to gratefully acknowledge the support of the citizens of Canada, through the Canadian Institutes of Health Research, in the preparation of this manuscript.

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.021
metaresearch head score (Gemma)0.107
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: Commentary
Teacher disagreement score0.074
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0210.107
Meta-epidemiology (narrow)0.0030.002
Meta-epidemiology (broad)0.0030.004
Bibliometrics0.0030.004
Science and technology studies0.0070.019
Scholarly communication0.0090.023
Open science0.0100.005
Research integrity0.0740.098
Insufficient payload (model declined to judge)0.0060.007

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.360
GPT teacher head0.570
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations12
Published2006
Admission routes2
Has abstractyes

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