Clinical effectiveness of bevacizumab in patients with recurrent brain tumours: A population-based evaluation
Bibliographic record
Abstract
Background Bevacizumab is an antiangiogenic agent active in patients with recurrent malignant gliomas. However, evidence for its clinical efficacy is relatively limited so that bevacizumab is approved for this indication in Canada and the United States, but not in the European Union. We reviewed the effectiveness of bevacizumab in patients with recurrent brain tumour using a large population database. Methods This was a retrospective, multicentre, study conducted at the BC Cancer Agency, a public cancer care organisation for the residents of the Canadian province of British Columbia. Cases were identified from the provincial registry and drug database. Patients were eligible if they were treated with bevacizumab with or without lomustine or etoposide for recurrent brain tumour between April 2011 and March 2014. The primary end points were progression-free survival. Secondary endpoints were overall survival and objective response rate. Results A total of 160 patients were included, with a median age of 55 years. The most common diagnosis was glioblastoma multiforme (70.6%), followed by oligodendroglioma (10.6%). Half of the patients had prior metronomic dosing of temozolomide. The median duration of therapy was 3 months. The median progression-free survival was 4.0 months and the 6-month progression-free survival was 29.4%. The median overall survival was 7 months and the 9-month and 12-month overall survival was 28.1% and 20.6%, respectively. The objective response rate was 23.1%. The most common documented reason for bevacizumab discontinuation was disease progression (66.9%), followed by toxicity (6.9%). Conclusions Bevacizumab therapy seems to be effective in delaying disease progression in patients with recurrent brain tumour, but with limited benefits on the overall survival, when used outside the clinical trial setting.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".