Treatment with denosumab reduces secondary fracture risk in women with postmenopausal osteoporosis
Bibliographic record
Abstract
OBJECTIVES: A history of prior fracture is one of the strongest predictors of a future fragility fracture. In FREEDOM, denosumab significantly reduced the risk of new vertebral, non-vertebral, and hip fractures. We carried out a post-hoc analysis of FREEDOM to characterize the efficacy of denosumab in preventing secondary fragility fractures in subjects with a prior fracture. METHODS: A total of 7808 women aged 60-90 years with a bone mineral density T-score of less than - 2.5 but not less than - 4.0 at either the lumbar spine or total hip were randomized to subcutaneous denosumab 60 mg or placebo every 6 months for 36 months. The anti-fracture efficacy of denosumab was analyzed by prior fracture status, to assess secondary fragility fracture, and by subject age, prior fracture site and history of prior osteoporosis medication use. RESULTS: A prior fragility fracture was reported for 45% of the overall study population. Compared with placebo, denosumab significantly reduced the risk of a secondary fragility fracture by 39% (incidence, 17.3% vs. 10.5%; p < 0.0001). Similar results were observed regardless of age or prior fracture site. In the overall population, denosumab significantly reduced the risk of a fragility fracture by 40% (13.3% vs. 8.0%; p < 0.0001), with similar results observed regardless of history of prior osteoporotic medication use. CONCLUSIONS: Denosumab reduced the risk of fragility fractures to a similar degree in all risk subgroups examined, including those with prior fragility fractures. Identifying and treating high-risk individuals could help to close the current care gap in secondary fracture prevention.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".