Lessons learned from contemporary glioblastoma randomized clinical trials through systematic review and network meta-analysis: part 2 recurrent glioblastoma
Bibliographic record
Abstract
Abstract Background There exists no consensus standard of treatment for patients with recurrent glioblastoma (GB). Here we used a network meta-analysis on treatments from randomized control trials (RCTs) to assess the effect on overall survival (OS) and progression-free survival (PFS) to determine if any consensus treatment can be determined for recurrent GB. Methods We included all recurrent GB RCTs with at least 20 patients in each arm, and for whom patients underwent standard of care at the time of their GB initial diagnosis. Our primary outcome was OS, with secondary outcomes including PFS and adverse reactions. Hazard ratio (HR) and its 95% confidence interval (CI) of the comparison of study arms regarding OS and PFS were extracted from each paper. For comparative efficacy analysis, we utilized a frequentist network meta-analysis, an extension of the classic pair-wise meta-analysis. We followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses. Results Fifteen studies were included representing 29 separate treatment arms and 2194 patients. In our network meta-analysis, combination treatment with tumor-treating field and Vascular endothelial growth factor (VEGF) inhibitor ranked first in improving OS (P = .80). Concomitant anti-VEGF and Lomustine treatment was superior to Lomustine alone for extending PFS (HR 0.57, 95% CI 0.41–0.79) and ranked first in improving PFS compared to other included treatments (P = .86). Conclusions Our analysis highlights the numerous studies performed on recurrent GB, with no proven consensus treatment that is superior to the current SOC. Intertrial heterogeneity precludes drawing strong conclusions, and confidence analysis was low to very low. Further confirmation by future trials is recommended for our exploratory results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.080 | 0.019 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".