Denosumab Versus Bisphosphonates for the Prevention of the Vertebral Fractures in Men with Osteoporosis: An Updated Network Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: The efficacy of the prevention of vertebral fractures in men with osteoporosis by treatment with denosumab is debated. This study aimed to update the comparative effectiveness of denosumab and bisphosphonates for preventing vertebral fractures in men with osteoporosis. METHODS: We searched PubMed, EMBASE, and Cochrane Central Register of Controlled Trials for randomized controlled trials that enrolled men with osteoporosis. Fixed-effects network meta-analysis was performed to evaluate the risk of vertebral fractures, and the relative risk (RR) and 95% confident interval (CI) values were calculated. RESULTS: Sixteen studies were included, and the identified bisphosphonates were risedronate, alendronate, zoledronic acid, and ibandronate. Compared with placebo or control, a significant reduction in vertebral fractures was observed for denosumab (RR 0.30, 95%CI 0.13 0.68), risedronate (RR 0.39, 95%CI 0.19 0.77), and zoledronic acid (RR 0.45, 95%CI 0.21 0.98). According to the surface under the cumulative ranking curve (SUCRA), denosumab was the most effective one among the included agents for the risk reduction of vertebral fracture. However, compared with each bisphosphonate, the RR values of denosumab were not significant [RR 0.78 (95%CI 0.25 2.43) vs. risedronate, RR 0.55 (95%CI 0.18 1.75) vs. alendronate, RR 0.66 (95%CI 0.19 2.32) vs. zoledronic acid and RR 1.12 (95%CI 0.08 14.83) vs. ibandronate]. CONCLUSION: Denosumab effectively reduced the risk of vertebral fractures in men with osteoporosis, and this effect was comparable to that of bisphosphonates.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.046 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".