and the Hereditary Ovarian Cancer Clinical Study Group Summary
Bibliographic record
Abstract
Background Several of the known risk factors for ovarian cancer are thought to act through their eff ects on ovulation and the menstrual cycle, such as parity, breastfeeding, and use of oral contraceptives. We aimed to assess the eff ect of these three risk factors, and of tubal ligation, on the risk of ovarian cancer in women who carry a mutation in the BRCA1 or BRCA2 genes. Methods We did a matched case-control study in women who were found to carry a pathogenetic mutation in BRCA1 or BRCA2. Participants were derived from a population-based study of ovarian cancer in Ontario, Canada, and from an international registry of mutation carriers based in Toronto, ON, Canada. All participants completed a written questionnaire that detailed their reproductive history. Women with invasive ovarian cancer and controls were matched on year of birth, country of residence, mutation (BRCA1 or BRCA2), and history of breast cancer. The odds ratios and 95 % CI for ovarian cancer were estimated with respect to use of oral contraceptives, parity, breastfeeding, and tubal ligation. Findings Questionnaires were completed by 799 women with a history of invasive ovarian cancer (670 with BRCA1 mutations, 128 with BRCA2 mutations, and one with a mutation in both genes), and controls were 2424 women without ovarian cancer (2043 with BRCA1 mutations, 380 with BRCA2 mutations, and one with a mutation in both genes). Use of oral contraceptives reduced the risk of ovarian cancer in carriers of BRCA1 mutations (odds ratio 0·56
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".