Infertility and the Risk of Cardiovascular Disease: Findings From the Study of Women’s Health Across the Nation (SWAN)
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) is the leading cause of death in women globally. In recent years, attention has turned to infertility and pregnancy-related events as potential markers for early mortality and future CVD. METHODS: The Study of Women's Health Across the Nation (SWAN) is an ongoing longitudinal cohort study of women's health. Women aged 42-52 years with a uterus and ≤ 1 intact ovary, a menstrual period, and no hormone medications within 3 months before enrollment were eligible. Infertility was self-reported and defined as the inability to achieve pregnancy after 12 months of trying to conceive, or use of fertility medications for > 1 month. Outcomes included development of metabolic syndrome over a 7-year follow-up, and any atherosclerotic CVD event (ie, stroke, angina, myocardial infarction) over a 10-year follow-up. Cox proportional hazards models were used to calculate hazard ratios (HRs) for metabolic syndrome and CVD events in participants with infertility, with adjustment for relevant covariates. Participants without infertility were used as the comparison group. RESULTS: We included 2370 participants in the analysis of metabolic syndrome risk, and 2809 participants were included in the analysis of CVD event risk. Participants with self-reported infertility did not have a higher risk of developing metabolic syndrome (HR, 0.91; 95% confidence interval, 0.71-1.15) or experiencing CVD events (HR, 0.79; 95% confidence interval, 0.52-1.21) after adjusting for relevant covariates. CONCLUSIONS: Infertility was not associated with development of metabolic syndrome or CVD events in women; further research is required to investigate the effects of specific causes of infertility and fertility treatments on CVD outcomes.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".