Further Nonvertebral Fracture Reduction Beyond 3 Years for Up to 10 Years of Denosumab Treatment
Bibliographic record
Abstract
CONTEXT: Evidence for further nonvertebral fracture (NVF) reductions with long-term antiresorptive therapy in osteoporosis is lacking. OBJECTIVE: To evaluate NVF risk reduction in subjects receiving ≤10 years of denosumab treatment. DESIGN: Phase 3, randomized, placebo-controlled, 3-year Fracture Reduction Evaluation of Denosumab in Osteoporosis Every 6 Months (FREEDOM) trial (NCT00089791) and its open-label 7-year extension (NCT00523341). SETTING: One hundred seventy-two study centers worldwide. PATIENTS: Women 60 to 90 years, lumbar spine or total hip bone mineral density T-scores <-2.5 (≥-4.0 at both). INTERVENTIONS: Subjects randomly assigned 1:1 denosumab 60 mg SC Q6M (long-term) or placebo (crossover) in FREEDOM; eligible subjects could enroll in the extension to receive denosumab 60 mg SC Q6M. MAIN OUTCOME MEASURES: NVF Exposure-adjusted subject incidence (per 100 subject-years) during denosumab treatment years 1 to 3 and 4 to 7 (all subjects) and years 4 to 10 (long-term only), and rate ratios (RRs) for years 4 to 7 or 4 to 10 vs 1 to 3. RESULTS: Among 4074 subjects (2343 long-term, 1731 crossover), NVF rates (95% CI) in all subjects were 2.15 (1.90 to 2.43) during years 1 to 3 and 1.53 (1.34 to 1.75) during years 4 to 7 of denosumab treatment [RR (95% CI) = 0.72 (0.61 to 0.86); P < 0.001]; in long-term only were 1.98 (1.67 to 2.34) during years 1 to 3 and 1.44 (1.24 to 1.66) during years 4 to 10 [RR = 0.74 (0.60 to 0.93); P = 0.008]. combined osteonecrosis of the jaw and atypical femoral fracture rate was 0.06. CONCLUSIONS: Long-term denosumab treatment, >3 and ≤10 years, was associated with further reductions in NVF rates compared with the first 3 years.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".