Cancer Risk After Exposure to Treatments for Ovulation Induction
Bibliographic record
Abstract
Uncertainty continues as to whether treatments for ovulation induction are associated with increased risk of cancer. The authors conducted a long-term population-based historical cohort study of parous women. A total of 15,030 women in the Jerusalem Perinatal Study who gave birth in 1974-1976 participated in a postpartum survey. Cancer incidence through 2004 was analyzed using Cox's proportional hazards models, controlling for age and other covariates. Women who used drugs to induce ovulation (n = 567) had increased risks of cancer at any site (multivariate hazard ratio (HR) = 1.36, 95% confidence interval (CI): 1.06, 1.74). An increased risk of uterine cancer was found among women treated with ovulation-inducing agents (HR = 3.39, 95% CI: 1.28, 8.97), specifically clomiphene (HR = 4.56, 95% CI: 1.56, 13.34). No association was noted between use of ovulation-inducing agents and ovarian cancer (age-adjusted HR = 0.61, 95% CI: 0.08, 4.42). Ovulation induction was associated with a borderline-significant increased risk of breast cancer (multivariate HR = 1.42, 95% CI: 0.99, 2.05). Increased risks were also observed for malignant melanoma and non-Hodgkin lymphoma. These associations appeared stronger among women who waited more than 1 year to conceive. Additional follow-up studies assessing these associations by drug type, dosage, and duration are needed.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".