Bilateral Oophorectomy and Breast Cancer Risk in<i>BRCA1</i>and<i>BRCA2</i>Mutation Carriers
Bibliographic record
Abstract
Background: Whether oophorectomy reduces breast cancer risk among BRCA mutation carriers is a matter of debate. We undertook a prospective analysis of bilateral oophorectomy and breast cancer risk in BRCA mutation carriers. Methods: Subjects had no history of cancer, had both breasts intact, and had information on oophorectomy status (n = 3722). Women were followed until breast cancer diagnosis, prophylactic bilateral mastectomy, or death. A Cox regression model was used to estimate the hazard ratios (HRs) and 95% confidence intervals (CIs) of breast cancer associated with oophorectomy (coded as a time-dependent variable). All statistical tests were two-sided. Results: Over a mean follow-up of 5.6 years, 350 new breast cancers were diagnosed. Among women with a BRCA1 or BRCA2 mutation, oophorectomy was not associated with breast cancer risk compared with women who did not undergo an oophorectomy. The age-adjusted hazard ratio associated with oophorectomy was 0.96 (95% CI = 0.73 to 1.26, P = 76) for BRCA1 and was 0.65 (95% CI = 0.37 to 1.16, P = 14) for BRCA2 mutation carriers. In stratified analyses, the effect of oophorectomy was statistically significant for breast cancer in BRCA2 mutation carriers diagnosed prior to age 50 years (age-adjusted HR = 0.18, 95% CI = 0.05 to 0.63, P = 007). Oophorectomy was not associated with risk of breast cancer prior to age 50 years among BRCA1 mutation carriers (age-adjusted HR = 0.79, 95% CI = 0.55 to 1.13, P = 51). Conclusions: Findings from this large prospective study support a role of oophorectomy for the prevention of premenopausal breast cancer in BRCA2, but not BRCA1 mutation carriers. These findings warrant further evaluation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 0.000 |
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".