Factors influencing ovulation and the risk of ovarian cancer in <scp><i>BRCA1</i></scp> and <scp><i>BRCA2</i></scp> mutation carriers
Bibliographic record
Abstract
The role of the lifetime number of ovulatory cycles has not been evaluated in the context of BRCA-associated ovarian cancer. Thus, we conducted a matched case-control study to evaluate the relationship between the cumulative number of ovulatory cycles (and contributing components) and risk of developing ovarian cancer in BRCA mutation carriers (1,329 cases and 5,267 controls). Information regarding reproductive and hormonal factors was collected from a routinely administered questionnaire. Conditional logistic regression was used to evaluate all associations. We observed a 45% reduction in the risk of developing ovarian cancer among women in the lowest vs. highest quartile of ovulatory cycles (OR = 0.55; 95% CI 0.41-0.75, p = 0.0001). Breastfeeding for more than 12 months was associated with a 38% (95% CI 0.48-0.79) and 50% (95% CI 0.29-0.84) reduction in risk among BRCA1 and BRCA2 mutation carriers, respectively. For oral contraceptive use, maximum benefit was seen with five or more years of use among BRCA1 mutation carriers (OR = 0.50; 95% CI 0.40-0.63) and three or more years for BRCA2 mutation carriers (OR = 0.42; 95% CI 0.22-0.83). Increasing parity was associated with a significant inverse trend among BRCA1 (OR = 0.87; 95% CI 0.79-0.96; p-trend = 0.005) but not BRCA2 mutation carriers (OR 0.98; 95% CI 0.81-1.19; p-trend = 0.85). A later age at menopause was associated with an increased risk in women with a BRCA1 mutation (OR trend = 1.18; 95% CI 1.03-1.35; p = 0.02). These findings support an important role of breastfeeding and oral contraceptive use for the primary prevention of ovarian cancer among women carrying BRCA mutations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".