Bisphosphonates Versus Denosumab for Prevention of Pathological Fracture in Advanced Cancers With Bone Metastasis: A Meta-analysis of Randomized Controlled Trials
Bibliographic record
Abstract
BACKGROUND: Metastasis to the bone is one of the most common complications associated with advanced cancer. Patients with bone metastases are at risk of devastating skeletal related events, including pathological fractures. PURPOSE: The aim of this study was to analyze the efficacy of zoledronic acid (ZA) versus denosumab in the prevention of pathological fractures in patients with bone metastases from advanced cancers by evaluating all available randomized controlled trials (RCTs) on this subject. METHODS: A systematic search of electronic databases (PubMed and MEDLINE) was performed to identify all published RCTs comparing ZA with denosumab in prevention of pathological fractures in bone metastases. Risk of bias of the studies was assessed. The primary outcomes evaluated were pathological fractures. RESULTS: Four RCTs (7,320 patients) were included. Denosumab was superior to ZA in reducing the likelihood of pathological fractures, when all tumor types were combined (odds ratio [OR] 0.86, 95% confidence interval [CI], 0.74 to 0.99, P = 0.04). Denosumab was favored, although not statistically significant, over ZA in endodermal origin (breast and prostate) (OR 0.85, 95% CI, 0.68 to 1.05, P = 0.13) and mesodermal origin tumors (solid tumors and multiple myeloma) (OR 0.87, 95% CI, 0.71 to 1.06, P = 0.16). DISCUSSION: Denosumab moderately reduces the likelihood of pathological fractures in comparison to ZA in patients with bone metastases with statistical significance. When pathological fractures were grouped by tumor origin (endodermal or mesodermal), no statistical difference was observed between denosumab and ZA. Further long-term studies are needed to confirm the effectiveness of these treatment regimens.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.021 | 0.048 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".