Prophylactic bilateral mastectomy
Bibliographic record
Abstract
BACKGROUND: Many women who are at an elevated risk of developing breast carcinoma choose prophylactic mastectomy to decrease their risk. We conducted a population-based study to review the indications for, and patterns of practice of prophylactic mastectomy in Ontario, Canada, since 1991. METHODS: A medical chart review was conducted at 33 hospitals that were identified as having conducted at least one prophylactic mastectomy. All bilateral mastectomy patients with no diagnosis of invasive or in situ breast carcinoma were eligible. RESULTS: The number of prophylactic bilateral mastectomies performed varied from 6 to 19. The mean age of women undergoing prophylactic mastectomy was 43.5 years. Eighty percent of the women had prophylactic mastectomy performed because of a family history of breast carcinoma (89 of 99) or because of a known BRCA1 or BRCA2 mutation (10 of 99). Twenty percent of the women had no family history, but had the surgery for other benign breast conditions. Women with a family history of breast carcinoma were much more likely to have a total mastectomy (89%) than a subcutaneous mastectomy (11%). Sixty percent of the women had reconstructive surgery following mastectomy. CONCLUSIONS: Prophylactic mastectomy is not performed on a large scale. The introduction of genetic testing for BRCA1 and BRCA2 has the potential to change the patterns of practice for prophylactic mastectomy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".