Progression Pattern and Adverse Events with Bevacizumab in Glioblastoma
Bibliographic record
Abstract
Background: The use of bevacizumab in the management of glioblastoma multiforme (gbm) remains controversial. In Canada, bevacizumab is approved for the treatment of recurrent gbm. We describe a pattern of progression across treatment lines in gbm. Methods: During 2008–2014, 64 patients diagnosed with gbm were treated with bevacizumab at McGill University hospitals. Of those patients, 30 (46.9%) received bevacizumab in the first line (B1L), and 34 (53.1%) received it in the second line and beyond (B2L+). The average length of treatment with bevacizumab was 24.4 weeks (range: 0–232.7 weeks). The patterns of progression were categorized as local, distant, diffuse, multifocal, or multi-pattern. Results: Local progression was seen in 46.7% of B1L patients and 26.5% of B2L+ patients, distant in 3.3% and 2.9%, diffuse in 20% and 47%, multifocal in 10% and 8.8%, and multi-pattern in 3.3% and 11.8%. No differences between the groups were observed for the distant (p = 0.3) or diffuse (p = 0.4) patterns. Grades 3 and 4 adverse events in the B1L and B2L+ groups were fatigue (33.3% vs. 17.6% respectively), hypertension (26.7% vs. 5.9%), thrombocytopenia (26.7% vs. 11.8%), neutropenia (26.7% vs. 11.8%), anemia (23.3% vs. 11.8%), leucopenia (20% vs. 8.8%), deep vein thrombosis (23.3% vs. 5.9%), seizure (16.7% vs. 8.8%), brain hemorrhage (6.7% vs. <1%), and delayed wound healing (6.7% vs. 2.9%). More total grades 3 and 4 adverse events occurred in the B1L group (p = 0.000519). Conclusions: In our cohort, patterns of progression were not different in B1L and B2L+ patients. Moreover, both groups experienced similar adverse events, although more grades 3 and 4 events occurred in the B1L group, implying that severe adverse events in B1L patients could negatively affect survival outcomes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".