Valuing Evidence: Bias and the Evidence Hierarchy of Evidence-Based Medicine
Bibliographic record
Abstract
Proponents of evidence-based medicine (EBM) suggest that a hierarchy of evidence is needed to guide medical research and practice. Given a variety of possible evidence hierarchies, however, the particular version offered by EBM needs to be justified. This article argues that two familiar justifications offered for the EBM hierarchy of evidence-that the hierarchy provides special access to causes, and that evidence derived from research methods ranked higher on the hierarchy is less biased than evidence ranked lower-both fail, and that this indicates that we are not epistemically justified in using the EBM hierarchy of evidence as a guide to medical research and practice. Following this critique, the article considers the extent to which biases influence medical research and whether meta-analyses might rescue research from the influence of bias. The article concludes with a discussion of the nature and role of biases in medical research and suggests that medical researchers should pay closer attention to social mechanisms for managing pervasive biases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.253 | 0.426 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.022 | 0.012 |
| Science and technology studies | 0.008 | 0.096 |
| Scholarly communication | 0.026 | 0.032 |
| Open science | 0.005 | 0.018 |
| Research integrity | 0.011 | 0.015 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".