Oophorectomy after Menopause and the Risk of Breast Cancer in <i>BRCA1</i> and <i>BRCA2</i> Mutation Carriers
Bibliographic record
Abstract
BACKGROUND: To evaluate the effect of the cumulative number of ovulatory cycles and its contributing components on the risk of breast cancer among BRCA mutation carriers. METHODS: We conducted a matched case-control study on 2,854 pairs of women with a BRCA1 or BRCA2 mutation. Conditional logistic regression was used to estimate the association between the number of ovulatory cycles and various exposures and the risk of breast cancer. Information from a subset of these women enrolled in a prospective cohort study was used to calculate age-specific breast cancer rates. RESULTS: The annual risk of breast cancer decreased with the number of ovulatory cycles experienced (ρ = -0.69; P = 0.03). Age at menarche and duration of breastfeeding were inversely related with risk of breast cancer among BRCA1 (P(trend) < 0.0001) but not among BRCA2 (P(trend) ≥ 0.28) mutation carriers. The reduction in breast cancer risk associated with surgical menopause [OR, 0.52; 95% confidence interval (CI), 0.40-0.66; P(trend) < 0.0001] was greater than that associated with natural menopause (OR, 0.81; 95% CI, 0.62-1.07; P(trend) = 0.14). There was a highly significant reduction in breast cancer risk among women who had an oophorectomy after natural menopause (OR, 0.13; 95% CI, 0.02-0.54; P = 0.006). CONCLUSIONS: These data challenge the hypothesis that breast cancer risk can be predicted by the lifetime number of ovulatory cycles in women with a BRCA mutation. Both pre- and postmenopausal oophorectomy protect against breast cancer. IMPACT: Understanding the basis for the protective effect of oophorectomy has important implications for chemoprevention.
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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 | 0.000 |
| 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) | 0.001 | 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".