A systematic review and meta-analysis of effects of menopausal hormone therapy on cardiovascular diseases
Bibliographic record
Abstract
A systematic review and meta-analysis of randomized controlled trials (RCTs) and observational studies was conducted to assess the association between menopausal hormone therapy and cardiovascular disease. The PubMed and EMBASE databases were searched for articles published from 2000 to 2019, using review methods based on a previous Cochrane review. Quality assessment of RCTs and observational studies was conducted using the Jadad scale and the Newcastle-Ottawa Scale, respectively. A total of 26 RCTs and 47 observational studies were identified. The study populations in the RCTs were older and had more underlying diseases than those in the observational studies. Increased risks of venous thromboembolism [summary estimate (SE), 95% confidence interval (CI): RCTs, 1.70, 1.33-2.16; observational studies, 1.32, 1.13-1.54] were consistently identified in both study types, whereas an increased risk of stroke in RCTs (SE: 1.14, 95% CI: 1.04-1.25) and a decreased risk of myocardial infarction in observational studies (SE: 0.79, 95% CI: 0.75-0.84) were observed. Differential clinical effects depending on timing of initiation, underlying disease, regimen type, and route of administration were identified through subgroup analyses. These findings suggest that underlying disease and timing of initiation should be carefully considered before starting therapy in postmenopausal 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 | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.024 |
| Bibliometrics | 0.013 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".