Effect of conjugated estrogens/bazedoxifene on postmenopausal bone loss: pooled analysis of two randomized trials
Bibliographic record
Abstract
OBJECTIVE: Conjugated estrogens/bazedoxifene reduces vasomotor symptoms and prevents postmenopausal bone loss without stimulating the breast and endometrium. We analyzed changes in bone mineral density (BMD) and bone markers using pooled data from two phase-3 trials. METHODS: Selective Estrogens, Menopause, and Response to Therapy (SMART)-1 and SMART-5 were randomized, double-blind, placebo- and active-controlled studies conducted in postmenopausal nonhysterectomized women. BMD and turnover marker data were pooled for women given conjugated estrogens (0.45 or 0.625 mg) plus bazedoxifene 20 mg or placebo over 12 months. Sensitivity analyses were conducted using baseline Fracture Risk Assessment Tool score, age, years since menopause, body mass index, race, and geographic region. RESULTS: There were 1,172 women, mean age 54.9 years, mean 6.21 years since menopause, mean lumbar spine, and total hip T scores -1.05 and -0.58; 58.8% had a Fracture Risk Assessment Tool score less than 5% indicating low fracture risk. At 12 months, adjusted differences (vs placebo) in BMD change in the groups taking conjugated estrogens 0.45 or 0.625 mg plus bazedoxifene 20 mg were 2.3% and 2.4% for lumbar spine, 1.4% and 1.5% for total hip, and 1.1% and 1.5% for femoral neck (all P < 0.001 vs placebo). These increases were unrelated to baseline Fracture Risk Assessment Tool score, age, years since menopause, body mass index, or geographic region. Both doses reduced bone turnover markers (P < 0.001). CONCLUSIONS: Conjugated estrogens/bazedoxifene significantly improved BMD and turnover in a large population of younger postmenopausal women at low fracture risk and is a promising therapy for preventing postmenopausal bone loss.
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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 | 0.019 | 0.032 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 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".