Comparison of the Effect of Denosumab and Alendronate on BMD and Biochemical Markers of Bone Turnover in Postmenopausal Women With Low Bone Mass: A Randomized, Blinded, Phase 3 Trial
Bibliographic record
Abstract
Abstract Denosumab is a fully human monoclonal antibody that inhibits bone resorption by neutralizing RANKL, a key mediator of osteoclast formation, function, and survival. This phase 3, multicenter, double-blind study compared the efficacy and safety of denosumab with alendronate in postmenopausal women with low bone mass. One thousand one hundred eighty-nine postmenopausal women with a T-score ≤ −2.0 at the lumbar spine or total hip were randomized 1:1 to receive subcutaneous denosumab injections (60 mg every 6 mo [Q6M]) plus oral placebo weekly (n = 594) or oral alendronate weekly (70 mg) plus subcutaneous placebo injections Q6M (n = 595). Changes in BMD were assessed at the total hip, femoral neck, trochanter, lumbar spine, and one-third radius at 6 and 12 mo and in bone turnover markers at months 1, 3, 6, 9, and 12. Safety was evaluated by monitoring adverse events and laboratory values. At the total hip, denosumab significantly increased BMD compared with alendronate at month 12 (3.5% versus 2.6%; p < 0.0001). Furthermore, significantly greater increases in BMD were observed with denosumab treatment at all measured skeletal sites (12-mo treatment difference: 0.6%, femoral neck; 1.0%, trochanter; 1.1%, lumbar spine; 0.6%, one-third radius; p ≤ 0.0002 all sites). Denosumab treatment led to significantly greater reduction of bone turnover markers compared with alendronate therapy. Adverse events and laboratory values were similar for denosumab- and alendronate-treated subjects. Denosumab showed significantly larger gains in BMD and greater reduction in bone turnover markers compared with alendronate. The overall safety profile was similar for both treatments.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| 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".