Long-Term Effectiveness and Safety of Denosumab for Osteoporosis in Patients With Rheumatic Diseases
Bibliographic record
Abstract
Objective The long-term effectiveness of denosumab, an antireceptor activator of nuclear factor-κB ligand monoclonal antibody, for increasing bone mineral density (BMD) and reducing fracture risk in postmenopausal women with osteoporosis (OP) has been demonstrated; however, the long-term effectiveness and safety in patients with rheumatic diseases (RDs) remain unclear. Therefore, the present study investigated the long-term effectiveness and safety of denosumab for OP in patients with RDs. Methods This retrospective study included patients who received denosumab between August 2013 and August 2022. We evaluated BMD at the lumbar spine for up to 7 years and at the femur for up to 3 years. The effects of glucocorticoid (GC) usage, age, and renal function on BMD in patients receiving denosumab were assessed. The retention rate and adverse events were also evaluated. Results One hundred sixty-five patients with RDs were enrolled (median age 66.5 years, 92.1% female, 68.5% receiving GC therapy). Lumbar spine BMD significantly increased over 7 years ( P < 0.001), whereas femoral neck, trochanter, and total hip BMD significantly increased for up to 3 years ( P < 0.001). Lumbar spine BMD significantly increased regardless of GC dose, age, or renal dysfunction. The retention rate of denosumab at 7 years was 68.1%. The most common serious adverse event was infection. Two cases of osteonecrosis of the jaw and 10 new fractures were observed during treatment with denosumab. Conclusion The present study suggests that the long-term use of denosumab is an effective and generally safe option for increasing BMD in patients with RDs.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| 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".