Benefits of Estrogen Replacement for Skeletal Muscle Mass and Function in Post-Menopausal Females: Evidence from Human and Animal Studies
Bibliographic record
Abstract
Age related loss of skeletal muscle mass and strength accelerates with the onset of menopause in women. Recent evidence from human and animal studies provides compelling evidence for the role of estrogen based hormone replacement therapy (HRT) in maintaining and enhancing muscle mass and strength and protecting against muscle damage. The physiological mechanisms by which estrogen can positively influence skeletal muscle mass and strength and protect against post-damage inflammation and disruption are also beginning to emerge. These less well known benefits of estrogen for skeletal muscle coupled with other benefits of estrogen to bone and metabolic health in older females provide further incentives for HRT use to enhance overall health in post-menopausal women. New research also attests to the safety of shorter term HRT in younger post-menopausal females. Overall the benefits of HRT to muscle health and function could assist in offsetting age related loss of muscle mass and function and delay age related morbidity and their use for overall health benefits in aging females should continue to be evaluated.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".