Denosumab vs. Zoledronic Acid for Metastatic Bone Disease: A Comprehensive Systematic Review and Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Background: Metastatic bone disease (MBD) presents significant challenges in patient management, leading to skeletal-related events (SREs), compromised health-related quality of life, and heightened pain experiences. Denosumab (Dmab) and zoledronic acid (ZA) are bone-modifying agents (BMAs) commonly employed to mitigate the sequelae of MBD. Previous meta-analyses have assessed primary outcomes such as overall survival, pathological fractures, radiation to bone, and the time to SREs within studies. However, a single comprehensive analysis comparing their efficacy across multiple primary and secondary outcomes, as well as cost-effectiveness in specific cancer types, has not yet been conducted. Methods: A literature search identified relevant randomized controlled trials (RCTs), and the primary outcomes included overall survival, pathologic fractures, radiation to bone, and the time to SREs within studies. Secondary outcomes included adverse events, pain, analgesia usage, quality of life, and cost. Results: Meta-analysis revealed that Dmab effectively reduced the need for bone-targeted radiation therapy and was superior to ZA in delaying the time to SREs, except in multiple myeloma. Dmab also reduced pathological fracture incidences in breast cancer patients by 39%. Conclusions: Our analysis suggests that while both agents similarly impact overall survival and disease progression, Dmab offers advantages in SRE reduction and improved HRQoL and pain outcomes with lower rates of opioid usage, albeit with higher risks of hypocalcemia and osteonecrosis in some subgroups. The consensus on cost-effectiveness is mixed and varies based on the cancer type and healthcare system, with some studies favoring Dmab’s superior efficacy and safety, while others find ZA more cost-effective due to its lower cost. This study underscores the potential of Dmab as a preferred BMA for MBD management, especially for high-risk skeletal complications, while highlighting cancer-specific safety considerations. Further research is warranted to refine cancer-specific BMA use and optimize MBD management strategies.
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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 | 0.014 | 0.027 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 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".