Evidence‐Based Medicine or Cookbook Medicine? Addressing Concerns over the Standardization of Care
Bibliographic record
Abstract
Abstract Evidence‐based medicine (EBM), which advocates clinical decisions are based on evidence from medical research, has become an important ideal pursued in contemporary medicine. EBM relies on two key principles: the evidence hierarchy and clinical practice guidelines. Both principles have been fiercely criticized, and critics often invoke the term ‘Cookbook medicine’ to stress the dangers and limitations of EBM. This article reviews diverse critical literature on EBM by drawing on the newly proposed subfield of “Sociology of Standards.” It reframes the manifold critiques on EBM as concerns over the harm that standardization can bring about and demonstrates how empirical sociological studies have contributed to a better understanding of EBM's justificatory basis and regulatory impact. First, it discusses the ‘politics of Evidence’ inherent in EBM's epistemological basis, secondly, explores the actual ‘evidence‐base’ of its tools in practice, and third, addresses sociological debates on EBM's regulatory impact. In the concluding section, I argue that a ‘Sociology of Standards’ opens up new research avenues by allowing scholars to challenge – or at least empirically investigate – a host of dichotomies. By doing so, the role of the patient in EBM can be reframed to allow for more productive empirical investigations.
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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 | 0.125 | 0.175 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.109 |
| Scholarly communication | 0.017 | 0.033 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.017 | 0.024 |
| Insufficient payload (model declined to judge) | 0.003 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".