Fertility Drug Use and Mammographic Breast Density in a Mammography Screening Cohort of Premenopausal Women
Bibliographic record
Abstract
The widespread use of ovulation-inducing drugs to enhance fertility has raised concerns about its potential effects on breast cancer risk, as ovarian stimulation is associated with increases in estrogen and progesterone levels. We investigated the short-term relation between fertility drug use and mammographic breast density, a strong marker of breast cancer risk, among participants in the Group Health Breast Cancer Screening Program. Data linkage with Group Health automated pharmacy records identified 104 premenopausal women < 50 years old who obtained a mammogram during 1996--2006, within 2 years after a fertility drug dispensing. Premenopausal nonusers of fertility drugs were matched to users by age, body mass index, age at first birth, family history of breast cancer, past use of birth control hormones, race, and education (n = 1005). All mammograms were categorized for density according to the Breast Imaging Reporting Data System as entirely fat, scattered fibroglandular, heterogeneously dense, or extremely dense. Density in fertility drug users was equally likely as in nonusers to be rated entirely fat [odds ratio (OR), 0.83; 95% confidence interval (95% CI), 0.18-3.71], heterogeneously dense (OR, 1.09; 95% CI, 0.64-1.85), or extremely dense (OR, 0.93; 95% CI, 0.48-1.78) compared with scattered fibroglandular. In analyses restricted to fertility drug users, each additional month after the date of dispensing was associated with a 13% (95% CI for the OR, 1.01-1.27) increased odds of being categorized as heterogeneously/extremely dense compared with entirely fat/scattered fibroglandular (P = 0.04). Our results indicate no overall association between fertility drug use and mammographic density, but provide evidence that density may be lower in women more recently dispensed a fertility drug.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".