Menopausal hormone therapy for the primary prevention of chronic conditions. U.S. Preventive Services Task Force Recommendation Statement
Bibliographic record
Abstract
Since the early 20th century, scientists have been tantalized with the hypothesis that premenopausal health benefits in women can be preserved in postmenopausal women with the supplementation of exogenous hormone replacement therapy (HRT) of estrogen (alone/with progesterone). This hypothesis was shattered when the results of 2 large randomized controlled trials (RCTs), the Heart Estrogen/Progesterone Replacement Study (HERS) and Women's Health Initiative (WHI), reported an increased risk of adverse clinical outcomes including coronary heart disease, thromboembolic events, stroke, dementia, urinary incontinence, gallbladder disease, and breast cancer. However, since the WHI was published, firestorms of critique, controversy, and multiple subgroup analyses have populated the medical literature, predominantly focused around the analysis of the age of women at entry into the trials (hypothesized as an effect modifier) and suggesting lower-dose preparations including using bioidentical hormones. Recently, the U.S. Preventive Services Task Force (USPSTF) along with other professional groups have issued recommendations against the use of HRT to prevent chronic conditions. In this review, we review the most recent evidence, including the long-term follow-up data from RCTs along a multitude of health 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".