L'« Evidence Based Medicine » (EBM) : utile reflet de la réalité ou dangereux miroir de sorcière ?
Bibliographic record
Abstract
Modele dominant actuel de la Medecine dans bon nombre de pays au monde, l’«Evidence Based Medicine » (EBM), denommee ainsi, par Gordon Guyatt en 1992, a ete difficilement traduite en francais. On parlera surtout de Medecine fondee sur les preuves. Apparue au Canada, dans le sillage de l’epidemiologie clinique, l’EBM a ete developpee et promue tout d’abord comme outil d’apprentissage de la Medecine par le groupe de travail international « Evidence based working group ». Il s’agissait alors de procurer aux etudiants un outil capable de distinguer les etudes probantes au sein d’une litterature medicale qui commencait a foisonner et de developper du meme coup leur esprit critique a partir des articles scientifiques (sources primaires d’information). Mais l’EBM est bien vite devenue la theorie d’une pratique, une facon d’exercer la medecine clinique. On est ainsi passe rapidement d’une methode de tri de la litterature medicale « scientifique » selon un certain niveau de preuve, a l’exploitation des resultats des etudes issues de ce tri (source secondaire d’information) : revues systematiques de la litterature, recommandations de bonne pratique et autres documents en vue d'ameliorer les decisions cliniques mais aussi en tant qu'instruments de regulation des pratiques, des produits et des actes.
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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 | 0.094 | 0.195 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.010 | 0.006 |
| Science and technology studies | 0.003 | 0.027 |
| Scholarly communication | 0.028 | 0.029 |
| Open science | 0.005 | 0.009 |
| Research integrity | 0.010 | 0.015 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".