Measurements of Postmenopausal Serum Estradiol Levels and Cardiovascular Events: A Systematic Review
Bibliographic record
Abstract
Background: Cardiovascular disease (CVD) is the leading cause of death among female patients and its likelihood increases following menopause. However, whether estradiol levels are related to CVD remains unknown. We aimed to determine the association between serum estradiol levels and cardiovascular (CV) events in postmenopausal females. Methods: Electronic databases (MEDLINE, Embase) were searched systematically from inception to October 2022. Studies were eligible for inclusion if they included the following: (i) postmenopausal females; (ii) examination of the association between total serum estradiol levels and CV events (CV mortality, CVD, coronary heart disease, myocardial infarction, stroke, venous thromboembolism, heart failure, and CV hospitalization); (iii) original data (randomized controlled trial, quasi-experimental, cohort, case-control, or cross-sectional study). A narrative synthesis was completed because the data were not amenable to meta-analysis. Results: Of the 9026 citations retrieved, 8 articles were included, representing a total of 5635 women. The risk-of-bias was fair, and considerable heterogeneity was present. In those not using menopausal hormone therapy, 3 studies demonstrated mixed results between estradiol levels and risk of coronary heart disease, and 1 study showed that higher estradiol levels were associated with an increased risk of myocardial infarction. No significant associations were present between estradiol levels and the remaining events (ie, CV mortality, heart failure, CVD, and stroke). Conclusions: The association between serum estradiol levels and CV events in postmenopausal females remains unclear. Further studies assessing this association are warranted, given the elevated CVD risk in this population.
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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 | 0.006 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.007 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".