Analysis of Bevacizumab Treatment Practices, Survival and Quality of Life Outcomes in Recurrent Glioblastoma Patients
Bibliographic record
Abstract
OBJECTIVE: Bevacizumab is often used for treatment of recurrent glioblastoma (rGBM), yet there is no consensus on the best methods for its administration and timing. This retrospective study provides the largest Canadian cohort analysis of the experience and outcomes of patients with rGBM treated with bevacizumab. METHODS: We conducted a retrospective cohort study of patients aged 18 or older with rGBM treated in 6 tertiary care level cancer centers in British Columbia (BC) between 2011 and 2019. Patient demographics, tumor characteristics, treatment course, disease outcome and quality of life measures were collected. Overall survival (OS) and progression-free survival (PFS) were used as clinical outcomes. RESULTS: In our cohort of 272 patients, initiation of bevacizumab within 6 months of radiation treatment improved OS after bevacizumab initiation. Analysis of patients treated in high versus low-volume centers in BC suggested that patients in higher-volume centers were less likely to receive adjuvant chemotherapy with bevacizumab treatment, and more likely to have improved survival after bevacizumab initiation. Bevacizumab was shown in this study to appear to improve symptoms, preserve quality of life and reduce corticosteroid requirements. CONCLUSION: This Canadian cohort analysis characterizes bevacizumab treatment practices, survival and quality of life outcomes in rGBM patients in BC. Further investigations are needed to identify the demographic and biomarker characteristics of rGBM patients who would most benefit from bevacizumab treatment.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".