P.143 Eligibility criteria in glioma clinical trials: a systematic review and meta-analysis on selectivity, generalizability, and real-world applicability
Bibliographic record
Abstract
Background: Glioma trials may use selective criteria, limiting their generalizability to real-world patients. This systematic review and meta-analysis quantifies the prevalence of these criteria and evaluates their impact on trial outcomes, assessing whether reducing selectivity to improve generalizability and applicability is feasible without compromising safety or efficacy. Methods: 51 glioma trials were extracted from the National Clinical Trial (NCT) database on June 1st, 2024. Eligibility criteria were classified as selective—defined as likely to exclude patients who could benefit, or generalizable—justified due to potential harm or trial focus. The selective criteria were analyzed for correlation with median overall survival (mOS). Results: The average number of selective criteria per study was 6.8 (range: 0–14, median: 7). The most common were “No prior malignancy with a specified disease-free period” (N=29), “Exclusion based on Karnofsky score” (N=27), and “No prior brain radiotherapy” (N=16). Meta-analysis showed no significant correlation between the number of selective criteria and mOS (p = .327). Conclusions: Selective criteria are common in glioma trials, particularly exclusions based on prior malignancies, performance status, and past treatments. However, their lack of correlation with mOS indicates minimal impact on outcomes. These findings suggest reducing selectivity in trial criteria may improve generalizability and applicability without compromising safety or efficacy.
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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 | 0.119 | 0.268 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.031 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".