Treatment with denosumab reduces secondary fracture risk in women with postmenopausal osteoporosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it