“Evaluating normative epistemic frameworks in medicine: EBM and casuistic medicine”
Bibliographic record
Abstract
Since its inception in the early 1990s, evidence-based medicine (EBM) has become the dominant epistemic framework for Western medical practice. However, in light of powerful criticisms against EBM, alternatives such as casuistic medicine have been gaining support in both the medical and philosophical community. In the absence of empirical evidence in support of the claim that EBM improves patient outcomes, and in light of considerations that it is unlikely that such evidence will be forthcoming, another standard is needed to assess EBM against its alternatives. In this paper, I propose a set of criteria for this purpose based on Helen Longino's criteria for assessing the objectivity of a knowledge productive community. I then apply these criteria to assess EBM against a casuistic framework for medical knowledge. I argue that EBM's strict adherence to a hierarchical organization of knowledge can reasonably be expected to block it from fulfilling a high level of objectivity. A casuistic framework, on the other hand, because it emphasizes critical evaluation in conjunction with the flexibility of a case-based approach, could be expected to better facilitate a more optimal epistemic community.
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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 | 0.128 | 0.180 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.005 |
| Science and technology studies | 0.010 | 0.110 |
| Scholarly communication | 0.018 | 0.027 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".