ACTR-66. BEVACIZUMAB THERAPY FOR THE TREATMENT OF ADULT GLIOBLASTOMA: SYSTEMATIC REVIEW & META-ANALYSIS
Bibliographic record
Abstract
INTRODUCTION: Glioblastoma is the most common high-grade primary brain tumor in adults. Standard multi-modality treatment of glioblastoma often results in transient tumor control, but inevitably gives way to disease progression. The need for other therapeutic avenues led to interest in the anti-angiogenic therapy, namely bevacizumab, as a treatment for glioblastoma. We sought to determine the efficacy of bevacizumab as a treatment for glioblastoma. We conducted a literature search using the PubMed database and Google Scholar to identify randomized controlled trials (RCTs) since 2014 investigating the safety and efficacy of bevacizumab in the treatment of adult patients (18 years and older) with both newly diagnosed and recurrent glioblastoma. Only Level I data that reported progression-free survival (PFS) and overall survival (OS) were included for analysis. We identified 14 studies that met our criteria, reporting on a total of 3,192 patients. Our preliminary analysis finds that treatment with bevacizumab consistently prolongs PFS with a correspondent decrease in PFS hazard ratio (HR) for treatment groups that include bevacizumab versus those that do not. Bevacizumab had no significant effect on OS in patients with newly diagnosed or recurrent glioblastoma. Seven studies reported on MGMT status: four studies found that patients with methylated MGMT status had a consistently longer PFS and OS, corresponding with significantly lower HR for both variables, when compared to unmethylated groups. Our preliminary findings suggest that bevacizumab therapy is associated with a longer PFS in adult patients with glioblastoma, however, bevacizumab had an inconsistent effect on OS in this patient population. The differential response to bevacizumab in relation to MGMT methylation status and molecular subtype requires further analysis.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.028 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.006 | 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".