Pharmacological prevention of fractures in patients undergoing glucocorticoid therapies: a systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To perform a network meta-analysis (NMA) on the efficacy of antiosteoporotic interventions in the prevention of vertebral and non-vertebral fractures in adult patients taking glucocorticoids (GCs). METHODS: We performed NMAs based on a prospectively developed protocol. A librarian-assisted database search of MEDLINE, EMBASE, Web of Science, Cumulative Index of Nursing and Allied Health Literature (CINAHL), the Cochrane Central Register of Controlled Trials (CENTRAL) and Chinese databases was conducted for randomized controlled trials (RCTs) comparing antiosteoporotic interventions in adult patients taking GCs. Outcomes were vertebral and non-vertebral fracture incidences. RESULTS: We included 56 RCTs containing 6479 eligible patients in our analysis. We found that alendronate and teriparatide were associated with decreased odds of both vertebral and non-vertebral fractures. Denosumab and risedronate were associated with decreased odds of vertebral fractures, while etidronate, ibandronate and alfacalcidol were associated with decreased odds of non-vertebral fractures. We observed low network heterogeneity as indicated by the I2 statistic, and we did not detect evidence of publication bias. All outcomes were based on a moderate quality of evidence according to GRADE. CONCLUSION: Bisphosphonates, teriparatide and denosumab are associated with decreased odds of fracture in patients undergoing GC therapy. Vitamin D metabolites and analogues (e.g. alfacalcidol) may have greater anti-fracture efficacy compared with plain vitamin D. SYSTEMATIC REVIEW REGISTRATION: The International Prospective Register of Systematic Reviews (PROSPERO)-CRD42019127073.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.042 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".