Relationship between bone mineral density changes with denosumab treatment and risk reduction for vertebral and nonvertebral fractures
Bibliographic record
Abstract
Dual-energy X-ray absorptiometric bone mineral density (DXA BMD) is a strong predictor of fracture risk in untreated patients. However, previous patient-level studies suggest that BMD changes explain little of the fracture risk reduction observed with osteoporosis treatment. We investigated the relevance of DXA BMD changes as a predictor for fracture risk reduction using data from the FREEDOM trial, which randomly assigned placebo or denosumab 60 mg every 6 months to 7808 women aged 60 to 90 years with a spine or total hip BMD T-score < -2.5 and not < -4.0. We took a standard approach to estimate the percent of treatment effect explained using percent changes in BMD at a single visit (months 12, 24, or 36). We also applied a novel approach using estimated percent changes in BMD from baseline at the time of fracture occurrence (time-dependent models). Denosumab significantly increased total hip BMD by 3.2%, 4.4%, and 5.0% at 12, 24, and 36 months, respectively. Denosumab decreased the risk of new vertebral fractures by 68% (p < 0.0001) and nonvertebral fracture by 20% (p = 0.01) over 36 months. Regardless of the method used, the change in total hip BMD explained a considerable proportion of the effect of denosumab in reducing new or worsening vertebral fracture risk (35% [95% confidence interval (CI): 20%-61%] and 51% [95% CI: 39%-66%] accounted for by percent change at month 36 and change in time-dependent BMD, respectively) and explained a considerable amount of the reduction in nonvertebral fracture risk (87% [95% CI: 35% - >100%] and 72% [95% CI: 24% - >100%], respectively). Previous patient-level studies may have underestimated the strength of the relationship between BMD change and the effect of treatment on fracture risk or this relationship may be unique to denosumab.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".