Cardiovascular and physiological risk factors in women at mid-life and beyond
Bibliographic record
Abstract
Cardiovascular disease (CVD) is the leading cause of death in women. After menopause, sex-specific and gender-specific factors may play an important role in increasing CVD risk, with changes in sex hormones, body fat distribution, lipid and metabolic profile, and structural and functional vascular modifications. Premature and early-onset menopause are detrimental to cardiovascular health due to the early cessation of the protective effect of endogenous estrogen. An independent association of menopause with an increased risk of CVD has been documented in early menopause (<45 years). Sex-related differences are relevant in pharmacokinetics and pharmacodynamics; different enzyme formations, drug compatibility, efficacy, and side effects vary for different sexes. Despite some progress in sex and gender research in CVD, disparities remain. Menopausal hormone therapy (MHT) is available at mid-life for symptoms of menopause and may impact cardiovascular risk. Taken early, MHT may reduce CVD morbimortality. However, this is balanced against the risk of increased thrombosis. This paper reviews physiologic changes that contribute to cardiovascular risk in postmenopausal women and discusses clinical implications. Specifically, it explores the atheroprotective effects of estrogen and MHT and the associations between menopause with lipid levels, hypertension, body composition, and diabetes for women at mid-life and beyond.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".