Is denosumab associated with an increased risk for infection in patients with low bone mineral density? A systematic review and meta‐analysis of randomized controlled trials
Bibliographic record
Abstract
AIM: Denosumab increases bone mineral density through inhibition of the receptor activator of nuclear factor κ-Β ligand (RANKL). RANKL has known immunomodulatory effect. The largest study to date that reviewed denosumab efficacy in osteoporosis demonstrated an increased incidence of serious adverse events of infection (SAEI). We aimed to further evaluate risk of infection and SAEI in denosumab-treated patients. METHOD: PubMed, Embase, Cochrane Central Register of Controlled Trials, and Web of Science were searched for randomized controlled trials. Studies comparing denosumab 60 mg every 6 months with placebo or bisphosphonate for treatment of low bone mineral density were included. Trials were excluded for use of denosumab in cancer patients treated for skeletal-related events, immunosuppressed patient populations, or for comparison to teriparatide. Risk ratios (RR) with a 95% confidence interval (CI) were pooled using a fixed effects model, or a random effects model if heterogeneity occurred. RESULTS: Twenty-four randomized controlled trials (20 470 patients) were analyzed. An increased incidence of any infection (RR 1.11; 95% CI 1.02-1.20; P = 0.02) was observed in denosumab-treated patients compared with bisphosphonates, but not when compared with placebo. In contrast, a higher incidence of SAEI (RR 1.21; 95% CI 1.03-1.43; P = 0.02) was seen with denosumab when compared with placebo, but not compared with bisphosphonates. CONCLUSION: Denosumab-treated patients with low bone mineral density have slightly increased incidence of SAEI compared with placebo, but not when compared with bisphosphonates. Application of these results requires consideration of the entire body of data available regarding denosumab safety.
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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 | 0.016 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.026 | 0.036 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".