NCOG-24. REAL-WORLD ANALYSIS OF OUTCOMES OF PATIENTS RECEIVING BEVACIZUMAB FOR RECURRENT GLIOBLASTOMA IN BRITISH COLUMBIA
Bibliographic record
Abstract
Abstract BACKGROUND Bevacizumab (Bev) has been publicly funded in British Columbia (BC) since 2011 for treatment of recurrent glioblastoma (rGBM). We performed a retrospective outcomes assessment of patients with rGBM treated with Bev. METHODS Patients with rGBM treated at BC Cancer centers with Bev between January 2011 and December 2016 were reviewed. Patient demographics, tumor characteristics, treatment regimens, and dates of radiographic progression and death were collected. Kaplan-Meier method was used to assess survival, and comparisons were made using the log-rank test. RESULTS 138 patients were reviewed. There were 136 reported deaths with median PFS 3 months (CI95 = 2.5 - 3.5) and OS 7 months (CI95 = 6.1-8.0) from Bev initiation. 64% of patients on corticosteroids prior to Bev reduced their dose shortly after initiation. The majority of patients (72%) were treated with multiple lines of therapy prior to Bev, with a median time from chemoradiation to Bev initiation of 8 months (range 1-67). Patients started on Bev < 6 months from chemoradiation (prior to completion of adjuvant temozolomide) had improved OS compared to those who started Bev later (p = 0.05), but there was no association between extent of treatment prior to Bev and outcomes (p = 0.182). Addition of chemotherapy to Bev did not improve survival over Bev monotherapy (p = 0.175). CONCLUSIONS Despite limited benefits to overall survival, Bev is associated with reduction in corticosteroid use and likely improvement in quality of life. Bev combinations with chemotherapy did not confer survival advantage over Bev monotherapy. Furthermore, our results show that patients receiving Bev before completion of adjuvant chemotherapy have better outcomes, suggesting pseudoprogression may have prompted the therapeutic switch. Further research is required to optimize patient selection for and administration of Bev. Additional analysis of rGBM patients prescribed Bev until 2020 in BC is currently underway.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".