Bevacizumab in Recurrent High-Grade Gliomas: A Canadian Retrospective Study
Bibliographic record
Abstract
BACKGROUND: Bevacizumab has been used in recurrent glioblastoma (rGBM) since 2010 in Canada. Given its cost, potential toxicities, and unclear efficacy, further studies are required to better define suitable candidates for therapy. METHODS: A single-center retrospective review of patients started on bevacizumab for rGBM from 2012 to 2015 was performed. Patient demographics, tumor characteristics, treatment regimen, and dates of clinical progression and death were collected. Overall survival (OS) and progression-free survival (PFS) were used as clinical outcomes and estimates. Radiological response was assessed using modified Response Assessment in Neuro-Oncology criteria. RESULTS: A total of 80 patients were included. There were 67 reported deaths, and the median OS was 9.2 months (95% confidence interval [CI 95%]=7.0-10.1 months), with a 12-month OS of 31% (CI 95%=21.9-43.5%). Some 79 patients were included for analysis of clinical progression, among whom 61 had documented clinical progression. The median clinical PFS was 4.6 months (CI 95%=3.8-6.4 months), and the 6-month clinical PFS was 39% (CI 95%=29.0-52.9%). Addition of chemotherapy did not improve clinical outcomes. A total of 68 patients were included for radiological progression analysis, with 58 radiological progressions. The median radiological PFS was 5.8 months (CI 95%=4.2-6.7 months), and the 6-month radiological PFS was 46% (CI 95%=35.6-60.0%). CONCLUSIONS: This is the first reported Canadian experience with bevacizumab for rGBM. Our clinical outcomes are consistent with published data from multicenter phase II and III trials on bevacizumab in rGBM. More research is required to determine which subtype(s) of patients with rGBM could benefit from bevacizumab upon recurrence.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".