Infertility, Fertility Drugs, and Ovarian Cancer: A Pooled Analysis of Case-Control Studies
Bibliographic record
Abstract
Controversy surrounds the relations among infertility, fertility drug use, and the risk of ovarian cancer. The authors pooled interview data on infertility and fertility drug use from eight case-control studies conducted between 1989 and 1999 in the United States, Denmark, Canada, and Australia. Odds ratios and 95% confidence intervals were calculated, adjusting for age, race, family history of ovarian cancer, duration of oral contraception use, tubal ligation, gravidity, education, and site. Included in the analysis were 5,207 cases and 7,705 controls. Among nulligravid women, attempts for more than 5 years to become pregnant compared with attempts for less than 1 year increased the risk of ovarian cancer 2.67-fold (95% confidence interval (CI): 1.91, 3.74). Among nulliparous, subfertile women, neither any fertility drug use (odds ratio (OR) = 1.60, 95% CI: 0.90, 2.87) nor more than 12 months of use (OR = 1.54, 95% CI: 0.45, 5.27) was associated with ovarian cancer. Fertility drug use in nulligravid women was associated with borderline serous tumors (OR = 2.43, 95% CI: 1.01, 5.88) but not with any invasive histologic subtypes. Endometriosis (OR = 1.73, 95% CI: 1.10, 2.71) and unknown cause of infertility (OR = 1.19, 95% CI: 1.00, 1.40) increased cancer risk. These data suggest a role for specific biologic causes of infertility, but not for fertility drugs in overall risk for ovarian cancer.
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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 | 0.033 | 0.067 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.023 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".