Progression-Free but No Overall Survival Benefit for Adult Patients with Bevacizumab Therapy for the Treatment of Newly Diagnosed Glioblastoma: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Glioblastoma (GBM) is the most common high-grade primary brain tumor in adults. Standard multi-modality treatment of glioblastoma with surgery, temozolomide chemotherapy, and radiation results in transient tumor control but inevitably gives way to disease progression. The need for additional therapeutic avenues for patients with GBM led to interest in anti-angiogenic therapies, and in particular, bevacizumab. We sought to determine the efficacy of bevacizumab as a treatment for newly diagnosed GBM. 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 newly diagnosed GBM. Only Level Ι data that reported progression-free survival (PFS) and overall survival (OS) were included for analysis. Random effects meta-analyses on studies with newly diagnosed glioblastoma were conducted in R to estimate the pooled hazard ratio (HR) for PFS and OS. Six RCTs met requirements for meta-analysis, revealing a pooled estimate of PFS HR suggesting a 33% decreased risk of disease progression (HR 0.67, 95% CI, 0.58–0.78; p < 0.001) with bevacizumab therapy, but no effect on OS (HR = 1, 95% CI, 0.85–1.18; p = 0.97). A pooled estimate of the mean difference in OS months of −0.13 predicts little difference in time of survival between treatment groups (95% CI, −1.87–1.61). The pooled estimate for the mean difference in PFS months was 2.70 (95% CI, 1.89–3.50; p < 0.001). Meta-analysis shows that bevacizumab therapy is associated with a longer PFS in adult patients with newly diagnosed glioblastoma, but had an inconsistent effect on OS in this patient population.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.009 | 0.004 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".