Estrogen replacement and skeletal muscle: mechanisms and population health
Bibliographic record
Abstract
There is a growing body of information supporting the beneficial effects of estrogen and estrogen-based hormone therapy (HT) on maintenance and enhancement of muscle mass, strength, and connective tissue. These effects are also evident in enhanced recovery from muscle atrophy or damage and have significant implications particularly for the muscular health of postmenopausal women. Evidence suggests that HT will also help maintain or increase muscle mass, improve postatrophy muscle recovery, and enhance muscle strength in aged females. This is important because this population, in particular, is at risk for a rapid onset of frailty. The potential benefits of estrogen and HT relative to skeletal muscle function and composition combined with other health-related enhancements associated with reduced risk of cardiovascular events, overall mortality, and metabolic dysfunction, as well as enhanced cognition and bone health cumulate in a strong argument for more widespread and prolonged consideration of HT if started proximal to menopausal onset in most women. Earlier reports of increased health risks with HT use in postmenopausal women has led to a decline in HT use. However, recent reevaluation regarding the health effects of HT indicates a general lack of risks and a number of significant health benefits of HT use when initiated at the onset of menopause. Although further research is still needed to fully delineate its mechanisms of action, the general use of HT by postmenopausal women, to enhance muscle mass and strength, as well as overall health, with initiation soon after the onset of menopause should be considered.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".