Zoledronic Acid Versus Denosumab for Prevention of Spinal Cord Compression in Advanced Cancers With Spine Metastasis: A Meta-Analysis of Randomized Controlled Trials
Bibliographic record
Abstract
Study Design: Meta-analysis of randomized controlled trials (RCTs). Objectives: The aim was to analyze the efficacy of zoledronic acid (ZA) versus denosumab in the prevention of spinal cord compression in patients with spine metastases from advanced cancers, by evaluating all available 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 spinal cord compressions in spine metastases. Risk of bias of the studies was assessed. The primary outcomes evaluated were spinal cord compression. Results: Three RCTs (5274 patients) were included. Denosumab was not significantly superior to ZA in reducing the likelihood of spinal cord compression, when all tumor types were combined (odds ratio [OR] 0.92, 95% confidence interval [CI; 0.66, 1.28], P = .66). Denosumab was not significantly favored over ZA in endodermal origin (breast and prostate; OR 0.72, 95% CI [0.43, 1.19], P = .20) and mesodermal origin tumors (solid tumors and multiple myeloma; OR 1.10, 95% CI [0.72, 1.69], P = .66). Conclusion: Denosumab does not significantly reduce the likelihood of spinal cord compressions in comparison to ZA in patients with spine metastases. When spinal cord compressions were grouped by tumor origin (endodermal or mesodermal), there remained no significant difference between denosumab and ZA. Further long-term studies are needed to determine the effectiveness of these treatment regimens.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.038 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.051 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 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".