Bevacizumab Use for Recurrent High-Grade Glioma at McGill University Hospital
Bibliographic record
Abstract
BACKGROUND: Bevacizumab, a humanized recombinant anti-vascular endothelial growth factor antibody, was approved in Canada in 2010 for the treatment of high-grade glioma. We report the effectiveness and safety of bevacizumab in the treatment of patients with recurrent high-grade gliomas at a single institution. METHODS: Twenty-seven consecutive patients with high-grade glioma (anaplastic glioma and glioblastoma) at first or subsequent relapse were treated with bevacizumab alone or in combination with chemotherapy. The primary endpoint was progression-free survival (PFS) and secondary endpoints were objective response rate, six month PFS, overall survival (OS), and safety profile. RESULTS: The clinical benefit rate (complete and partial responses plus stable disease) was 59%. Median PFS was 4.3 (95% CI, 3.0-10.9) months, with a six month PFS rate of 43%. Median OS after current relapse was 8.9 (95% CI, 5.8-not reached) months. Ten episodes of grade 3/4 adverse events were observed in nine patients, including fatigue (n = 3), thrombocytopenia (n = 4), and myelotoxicity, febrile neutropenia, and pulmonary embolism (each n = 1). CONCLUSIONS: We consider the efficacy and safety profile of bevacizumab is comparable to other cohorts of patients treated for recurrent high-grade glioma at other international institutions.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".