NCOG-16. REAL-WORLD ANALYSIS OF OUTCOMES OF PATIENTS RECEIVING BEVACIZUMAB FOR RECURRENT GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Bevacizumab has been publicly funded in British Columbia (BC) since 2011 for treatment of recurrent glioblastoma (rGBM). We performed a retrospective analysis of patients with rGBM treated with bevacizumab to evaluate treatment practices and outcomes. 245 patients with rGBM treated at BC Cancer centers with bevacizumab between January 2011 and December 2019 were reviewed. Patient demographics, tumor characteristics, treatment regimens, and dates and type of radiographic progression and death were collected. Kaplan-Meier method was used to assess overall survival from time of bevacizumab initiation (BevOS), and comparisons were made using the log-rank test. Median OS was 7 months (CI95 = 6.28-7.73) from bevacizumab initiation. 66% of patients on corticosteroids prior to bevacizumab reduced their dose, and performance status was the same or improved in 84% of patients shortly after initiation, suggesting improvement in quality of life. Patients started on bevacizumab < 6 months from chemoradiation (prior to completion of adjuvant temozolomide) had improved BevOS compared to those who started bevacizumab later (p = 0.019), but there was no association between extent of treatment prior to bevacizumab and outcomes (p = 0.417). Additionally, patients who had local progression prior to bevacizumab initiation had improved BevOS compared to those who had distant progression (p = 0.004), suggesting that pseudoprogression may have prompted the therapeutic switch. Furthermore, though there were no differences in baseline characteristics for patients treated at high volume centers vs. low volume centers, patients treated at high volume centers had improved BevOS (p = 0.004), potentially due to differences in expertise between centers or other social determinants of health not captured in baseline demographics. Together, these data highlight the need for optimization of patient selection and bevacizumab administration in high and low volume centers.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".