Determining the Optimal Adjuvant Therapy for Improving Survival in Elderly Patients with Glioblastoma: A Systematic Review and Network Meta-analysis
Bibliographic record
Abstract
Abstract Purpose: Older patients with glioblastoma (GBM) are underrepresented in clinical trials. Several abbreviated and standard chemoradiotherapy regimens are advocated with no consensus on the optimal approach. Our objective was to quantitatively evaluate which of these regimens would provide the most favorable survival outcomes in older patients with GBM using a network meta-analysis. Experimental Design: MEDLINE, Embase, Google Scholar, and the Cochrane Library were searched. Patients >60 years of age with histologically confirmed GBM were included. Primary outcome of interest was the pooled HR from randomized controlled trials (RCTs). Secondary outcomes of interest included pooled HR from studies controlling for MGMT promoter methylation status, and safety. Results: Fourteen studies, including 5 RCTs, reporting 4,561 patients were included. Using highest quality data from RCTs, our network-based approach demonstrated that standard radiotherapy (SRT) and temozolomide (TMZ) provided similar survival benefit when compared with hypofractionated radiotherapy (HRT) and TMZ [HR = 0.90; 95% confidence interval (CI), 0.43–1.87], TMZ alone (HR 1.25; 95% CI, 0.69–2.26), HRT alone (HR = 1.34; 95% CI, 0.73–2.45), or SRT alone (HR = 1.43; 95% CI, 0.87–2.36). HRT-TMZ had the highest probability (85%) of improving survival in older patients with GBM followed by SRT-TMZ (72%). Pooled analysis of trials controlling for MGMT promoter methylation status demonstrated that TMZ monotherapy confers similar survival benefit to combined chemoradiotherapy. Conclusions: Statistical comparisons using a network approach demonstrates that the common treatment regimens for older patients with GBM in previous RCTs confer similar survival benefits. Adjustments for MGMT promoter methylation status demonstrated that radiotherapy alone was inferior to TMZ-based approaches. Head-to-head comparison of TMZ monotherapy to combined TMZ and radiation is warranted.
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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 | 0.016 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.029 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".