Journal of evaluation in clinical practice
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Confidence (or belief) that a therapy is effective is essential to practicing clinical medicine. GRADE, a popular framework for developing clinical recommendations, provides a means for assigning how much confidence one should have in a therapy's effect estimate. One's level of confidence (or "degree of belief") can also be modelled using Bayes theorem. In this paper, we look through both a GRADE and Bayesian lens to examine how one determines confidence in the effect estimate. ⋯ A rational thinker uses all available evidence to formulate beliefs. The GRADE criteria seem to suggest that we discard some of that information when other, more favoured information (eg, derived from clinical trials) is available. The GRADE framework should strive to ensure that the whole evidence base is considered when determining confidence in the effect estimate. The incremental value of such evidence on determining confidence in the effect estimate should be assigned in a manner that is theoretically or empirically justified, such that confidence is proportional to the evidence, both for and against it.
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Evidence-based medicine is the application of research findings to inform individual clinical decisions. There is a tension-both philosophical and practical-between the average result from a population study and the circumstances and needs of an individual patient. This personal account of "evidence-based" trauma care illustrates and explores this tension. ⋯ As Sir John Grimley Evans' warned, we should avoid using evidence-based guidelines in the manner of the fabled drunkard who searched under the lamp post for his key because that was where the light was, even though he knew he had lost his key somewhere else.
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The role of mechanistic evidence tends to be under-appreciated in current evidence-based medicine (EBM), which focusses on clinical studies, tending to restrict attention to randomized controlled studies (RCTs) when they are available. The EBM+ programme seeks to redress this imbalance, by suggesting methods for evaluating mechanistic studies alongside clinical studies. ⋯ Nevertheless, we argue that mechanistic evidence is central to all the key tasks in the drug approval process: in drug discovery and development; assessing pharmaceutical quality; devising dosage regimens; assessing efficacy, harms, external validity, and cost-effectiveness; evaluating adherence; and extending product licences. We recommend that, when preparing for meetings in which any aspect of drug approval is to be discussed, mechanistic evidence should be systematically analysed and presented to the committee members alongside analyses of clinical studies.
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The GRADE framework has been widely adopted as the preferred method for developing clinical practice recommendations. In the first article of our three part series examining the evolution of GRADE, we showed an absence (in the first two versions of GRADE) of a theoretical basis and/or empirical data to support why the presented criteria for determining the quality of evidence regarding the effect estimate and the components under consideration for determining the strength of the recommendation were included and other criteria/components excluded. Furthermore, often, it was not clear how to operationalize the included criteria/components (and integrate them) when using the framework. In part 2 of this series, we examine if version 3 of GRADE offered improvements on previous versions with respect to a justification scheme and how to operationalize the framework's criteria/components. ⋯ While version 3 reveals acknowledgement by the authors of GRADE that the framework is a work in progress, it still lacks a justification scheme (theoretical and/or empirical) to sustain it and clarity in its criteria/components to operationalize it. As was suggested in part 1, such issues limit one's ability to scientifically assess the appropriateness of GRADE for its stated purpose.
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In modern philosophy, the concept of truth has been problematized from different angles, yet in evidence-based health care (EBHC), it continues to operate hidden and almost undisputed through the linked concept of "bias." To prevent unwarranted relativism and make better inferences in clinical practice, clinicians may benefit from a closer analysis of existing assumptions about truth, validity, and reality. In this paper, we give a brief overview of several important theories of truth, notably the ideal limit theorem (which assumes an ultimate and absolute truth towards which scientific inquiry progresses), the dominant way truth is conceptualized in the discourse and practice of EBHC. We draw on Belgian philosopher Isabelle Stengers' work to demonstrate that bias means one thing if one assumes a world of hard facts "out there," waiting to be collected. ⋯ Most importantly, it casts doubt on the ideal limit theory as it applies to the single case scenario of the clinical encounter, the cornerstone of EBHC. To the extent that the goal of EBHC is to support inferencing in the clinical encounter, then the ideal limit as the sole concept of truth appears to be conceptually insufficient. We contend that EBHC could usefully incorporate a more pluralist understanding of truth and bias and provide an example how this would work out in a clinical scenario.