Is there a role for menopausal hormone therapy in the management of postmenopausal osteoporosis?
Bibliographic record
Abstract
We provide an evidence base and guidance for the use of menopausal hormone therapy (MHT) for the maintenance of skeletal health and prevention of future fractures in recently menopausal women. Despite controversy over associated side effects, which has limited its use in recent decades, the potential role for MHT soon after menopause in the management of postmenopausal osteoporosis is increasingly recognized. We present a narrative review of the benefits versus risks of using MHT in the management of postmenopausal osteoporosis. Current literature suggests robust anti-fracture efficacy of MHT in patients unselected for low BMD, regardless of concomitant use with progestogens, but with limited evidence of persisting skeletal benefits following cessation of therapy. Side effects include cardiovascular events, thromboembolic disease, stroke and breast cancer, but the benefit-risk profile differs according to the use of opposed versus unopposed oestrogens, type of oestrogen/progestogen, dose and route of delivery and, for cardiovascular events, timing of MHT use. Overall, the benefit-risk profile supports MHT treatment in women who have recently (< 10 years) become menopausal, who have menopausal symptoms and who are less than 60 years old, with a low baseline risk for adverse events. MHT should be considered as an option for the maintenance of skeletal health in women, specifically as an additional benefit in the context of treatment of menopausal symptoms, when commenced at the menopause, or shortly thereafter, in the context of a personalized benefit-risk evaluation.
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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 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".