The real-world effectiveness of anti-RANKL antibody denosumab on the clinical fracture prevention in patients with rheumatoid arthritis: The ANSWER cohort study
Bibliographic record
Abstract
OBJECTIVES: Rheumatoid arthritis (RA) is a chronic inflammatory disease characterized by localized and generalized bone loss. The risk of fractures is doubled in patients with RA. Denosumab, an anti-RANKL monoclonal antibody, is used for those with osteoporosis at high risk fracture and it has inhibitory effect of progressive bone erosion in patients with RA. While the increase in bone mineral density by denosumab has been reported in patients with RA, preventive effect of fracture by denosumab remains unknown. This study aimed to evaluate the efficacy of denosumab in treating clinical fracture risk in patients with RA. METHODS: Patients with RA who received denosumab treatment between 2013 and 2019 were retrospectively evaluated using the ANSWER (Kansai Consortium for the Well-Being of Rheumatic Disease Patients) cohort data. Fracture rates were evaluated between 0 and 6 months (reference period) versus > 6 months (post-reference period) of denosumab use. RESULTS: A total of 873 patients with RA received denosumab, and their characteristics were as follows: 88% females, mean age 68 years, and average disease duration 14.5 years. The hazard rates of all clinical fractures were 0.69 (per 100 person-years) in the reference period and 0.35 in the post-reference period, indicating a 49.2% decrease (p = 0.03). CONCLUSIONS: Denosumab suppresses the risk of clinical fractures in patients with RA.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".