Effect of Odanacatib on BMD and Fractures: Estimates from Bayesian Univariate and Bivariate Meta-analyses
Bibliographic record
Abstract
CONTEXT: Odanacatib (ODN), a selective cathepsin-K inhibitor, was found to increase bone mineral density (BMD); the effect on fractures is based on adverse event reports. OBJECTIVE: To estimate current effects and predict future effects of ODN on BMD and fractures. DATA SOURCES: Electronic databases (Medline, EMBASE, Cochrane Library), conference proceedings, and bibliographies. STUDY SELECTION: Trials that compared ODN 50 mg/wk to placebo for at least 1 year and reported changes in BMD or fractures. Meta-analysis: Two bone outcomes were pooled as independent and as joint outcomes in Bayesian univariate and bivariate random-effects models. DATA SYNTHESIS: Of 32 potentially eligible articles, six citations describing four trials (993 patients) were included. ODN for 3 years increased mean BMD at the lumbar spine by 5.0% (95% credible interval [CrI], 2.7, 7.5), total hip by 3.6% (95% CrI, 1.6, 5.9), and femoral neck (FN) by 3.6% (95% CrI, 1.6, 5.7). In a future trial of 3-year duration, the predicted mean increase in BMD, adjusted for the effect on fractures, was 4.9% for lumbar spine (95% CrI, 2.5, 7.4), 3.4% for total hip (95% CrI, 1.7, 5.2), and 3.5% for FN (95% CrI, 1.8, 5.3). After accounting for the effect on FN BMD, ODN for 3 years was associated with a population odds ratio of 0.38 (95% CrI, 0.1, 0.8). In a future trial, the odds ratio was 0.41 (95% CrI, 0.1, 1.1). The probability of benefit on fractures was 96-99%. The estimates remained robust in sensitivity analyses. CONCLUSIONS: Our analyses suggest that ODN will increase BMD and decrease all fractures in the fracture outcome trial; however, direct demonstration of this antifracture efficacy is needed.
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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 | 0.084 | 0.160 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.062 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| 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".