Management of bone metastasis with zoledronic acid: A systematic review and Bayesian network meta-analysis
Bibliographic record
Abstract
While considered the mainstay of treatment for specific bone metastases, ZA is used predominantly to treat osteolytic lesions. The purpose of this network meta-analysis is to compare ZA to other treatment options in its ability to improve specific clinical outcomes in patients with bone metastases secondary to any primary tumor. PubMed, Embase and Web of Science were systematically searched from inception to May 5th, 2022. Keywords used were solid tumor, lung neoplasm, kidney neoplasm, breast neoplasm, prostate neoplasm, ZA and bone metastasis. Every randomized controlled trial and non-randomized quasi-experimental study of systemic ZA administration for patients with bone metastases and any comparator were included. A Bayesian network meta-analysis was done on the primary outcomes including number of SREs, time to developing a first on-study SRE, overall survival, and disease progression-free survival. Secondary outcome was pain at 3, 6 and 12 months after treatment. Our search yielded 3861 titles with 27 meeting inclusion criteria. For the number of SRE, ZA in combination with chemotherapy or hormone therapy was statistically superior to placebo (OR 0.079; 95 % CrI: 0.022–0.27). For the time to the first on study SRE, the relative effectiveness of ZA 4 mg was statistically superior to placebo (HR 0.58; 95 % CrI:0.48–0.77). At 3 and 6 months, ZA 4 mg was significantly superior to placebo for reducing pain with a SMD of −0.85 (95 % CrI:-1.6, −0.0025) and −2.6 (95 % CrI:-4.7, −0.52) respectively. This systematic review shows the benefits of ZA in decreasing the incidence of SREs, increasing the time to the first on-study SRE, and reducing the pain level at 3 and 6 months.
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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 | 0.015 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| 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".