Bibliographic record
Abstract
The campaign promoting evidence-based medicine (EBM) shares similarities with Joseph Lister's 19th century campaign promoting surgical antisepsis. Both target medical practitioners as their primary clients, and both appeal to these clients in the manner of a public health programme, arguing that their standards are authoritative and relevant, that the clients fail to meet them, and that this failure is a problem that requires the clients to change. Both promote hygienic solutions to the problems that they identify, the problem of microbial pathogens in the case of Lister, and the process of clinical decision making in the case of EBM. Hygienic solutions aim to operationalize standards as case-independent procedures that can be performed as habits, and seek to identify instruments of purification against sources of contamination. EBM's solution is hygienic because it characterizes clinical decision-making behaviour as a source of contamination and because it promotes a general procedure designed to correct it. Comparing the EBM campaign to Lister's helps to explain why some clinicians have had trouble trying to implement EBM as a decision-making procedure in particular cases. EBM promotes a hygienic solution, but unlike Lister, does not confront a well-defined, empirically grounded problem. Some of the difficulties with EBM stem from a mismatch between its hygienic solution and the complexity and case-dependency of clinical decision making.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.401 | 0.159 |
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".