Pregnancy and Reproductive Risk Factors for Cardiovascular Disease in Women
Bibliographic record
Abstract
Beyond conventional risk factors for cardiovascular disease, women face an additional burden of sex-specific risk factors. Key stages of a woman's reproductive history may influence or reveal short- and long-term cardiometabolic and cardiovascular trajectories. Early and late menarche, polycystic ovary syndrome, infertility, adverse pregnancy outcomes (eg, hypertensive disorders of pregnancy, gestational diabetes, preterm delivery, and intrauterine growth restriction), and absence of breastfeeding are all associated with increased future cardiovascular disease risk. The menopause transition additionally represents a period of accelerated cardiovascular disease risk, with timing (eg, premature menopause), mechanism, and symptoms of menopause, as well as treatment of menopause symptoms, each contributing to this risk. Differences in conventional cardiovascular disease risk factors appear to explain some, but not all, of the observed associations between reproductive history and later-life cardiovascular disease; further research is needed to elucidate hormonal effects and unique sex-specific disease mechanisms. A history of reproductive risk factors represents an opportunity for comprehensive risk factor screening, refinement of cardiovascular disease risk assessment, and implementation of primordial and primary prevention to optimize long-term cardiometabolic health in women.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".