Il faudrait maintenant réévaluer le rapport bénéfice/risque du dépistage mammographique BMJ 2014. The Canadian National Breast Screening Study
Bibliographic record
Abstract
Le depistage des cancers a pour objectif une detection plus precoce des cancers mortels, un traitement a un stade precoce devant alors procurer au patient un benefice certain. Dans le cas du cancer du sein comme dans bien d'autres cancers, le depistage precoce detecte egalement les cancers moins offensifs qui seraient restes asymptomatiques toute la duree de vie de l'interessee. La detection de tels cancers est consideree comme du sur-diagnostic [2]. Comme il n'est pas possible de distinguer a priori les cancers potentiellement letaux et les moins offensifs, tous sont traites, source de sur-traitements et d'effets deleteres evitables. De precedentes etudes avaient conclu que, apres 15 ans de suivi, entre un cancer detecte sur trois [3] et un sur dix [4] etait en fait un sur-diagnostic. Le benefice a long terme du depistage systematique par mammographie necessite maintenant une reevaluation.
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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 | 0.011 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 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".