Survival Impact of Isocitrate Dehydrogenase (IDH)-Wildtype Histological Versus Molecular Glioblastoma: A Propensity Score-Matched Analysis
Bibliographic record
Abstract
Introduction Glioblastoma (GBM) is a highly aggressive brain tumor with a poor prognosis. Molecular classification has redefined GBM subtypes, but survival differences between histological GBM (h-GBM) and molecular GBM (mol-GBM) remain underexplored. This study uses propensity score matching (PSM) to compare survival outcomes, accounting for clinical confounders. Methods A retrospective cohort of isocitrate dehydrogenase (IDH)-wildtype GBM patients was analyzed using 1:1 nearest-neighbor PSM, balancing age (<64 or ≥64 years), O6-methylguanine-DNA methyltransferase (MGMT) methylation status, and radiotherapy (RT) dose. Kaplan-Meier estimates and log-rank tests were used to compare the overall survival (OS) and progression-free survival (PFS) between h-GBM and mol-GBM. Results The matched cohort included 26 h-GBM and 26 mol-GBM patients. Median OS was 15.2 months for mol-GBM vs. 14.2 months for h-GBM (p=0.238). Mol-GBM showed significantly longer PFS (11.8 vs. 8.0 months, p=0.005). In MGMT-unmethylated patients, mol-GBM had superior OS (15.2 vs. 12.7 months, p=0.030) and PFS (13.1 vs. 7.8 months, p<0.001). Higher RT dose (60 Gy/30 fractions) improved OS (22.1 vs. 13.3 months, p=0.010) and PFS (10.8 vs. 8.7 months, p=0.020) in mol-GBM. Conclusion Molecular classification significantly influences GBM prognosis, with mol-GBM demonstrating better PFS and selective OS benefits in MGMT-unmethylated patients. Higher RT doses enhance outcomes, supporting personalized treatment strategies. Validation in larger cohorts is needed.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".