© 1997 Canadian Medical Association (text and résumé) Is breast self-examination still necessary?
Bibliographic record
Abstract
Le Dr Bart J. Har-vey et des collègues font rapport dans ce numéro (page 1205) des résultats d’une étude de cas-témoins nichés. Les données de l’Étude nationale sur le dépistage du cancer du sein au Canada ont servi à déterminer les répercussions de l’auto-examen des seins sur la mortalité attribuable au cancer du sein. Ils ont constaté un lien entre des éléments de la technique d’auto-examen évalués objectivement et la baisse du risque de décès attribuable au cancer du sein et de métastases dis-tantes, mais n’en ont constaté aucun entre ces aspects et la fréquence d’auto-examen déclarée. Ces constatations révèlent le besoin d’information appropriée sur la technique d’auto-examen des seins. Before the introduction of organized breast screening programs, mostbreast cancer was detected by women themselves. Breast self-examina-tion was encouraged in the hope that early detection would reduce deaths from breast cancer and, along with clinical breast examination, is still considered an important adjunct to screening mammography.1 A review of 466 cases of breast cancer diagnosed subsequent to a negative screening mammog-raphy result revealed that most of the cancers were first detected by the women
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.454 | 0.180 |
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".