Bevacizumab-Based Therapy for Colorectal Cancer: Experience from a Large Canadian Cohort at the Jewish General Hospital between 2004 and 2009
Bibliographic record
Abstract
BACKGROUND: Before its regulatory approval in Canada, bevacizumab to treat patients with colorectal cancer (crc) was accessed through the Bevacizumab Expanded Access Trial and a special-access program at the Jewish General Hospital. We retrospectively evaluated patient outcomes in that large cohort. METHODS: All patients (n = 196) had metastatic crc, were bevacizumab-naïve, and received bevacizumab in combination with chemotherapy at the Jewish General Hospital between 2004 and 2009. We collected patient demographics and clinical characteristics; relevant medical history, disease stage and tumour pathology at diagnosis; type, duration, and line of therapy; grades 3 and 4 adverse events (aes), time to disease progression (ttp), and overall survival (os) from diagnosis. RESULTS: Median follow-up was 36.0 months. Median ttp was 8.0 months [95% confidence interval (ci): 7.0 to 9.0 months). Median os was 41.0 months (95% ci: 36.0 to 47.0 months). Of the 40 grades 3 and 4 bevacizumab-related aes experienced by 38 patients (19.4%), the most common were thrombocytopenia (n = 17), deep-vein thrombosis (n = 6), pulmonary embolism (n = 4), and hypertension (n = 3). CONCLUSIONS: In an expanded access setting, our data reflect the efficacy and safety of bevacizumab-based therapy in the controlled post-registration 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 | 0.001 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".