Bevacizumab in Combination with folfiri Chemotherapy in Patients with Metastatic Colorectal Cancer: An Assessment of Safety and Efficacy in the Province of Newfoundland and Labrador
Bibliographic record
Abstract
BACKGROUND: In 2005, bevacizumab was approved by Health Canada for patients with metastatic colorectal cancer (mCRC). Newfoundland and Labrador was one of the first Canadian provinces to fund this agent in combination with FOLFIRI (irinotecan, 5-fluorouracil, leucovorin) chemotherapy. In this analysis, the entire provincial bevacizumab sample for the first 2 years was assessed for overall safety and efficacy. METHODS: The medical records of 43 patients with mCRC who had received FOLFIRI with bevacizumab were identified and reviewed. The longitudinal data collection format that was adopted assessed occurrences of adverse events after each cycle of treatment. Toxicity outcomes such as gastrointestinal (GI) perforations, bleeding, diarrhea, myelosuppression, proteinuria, and venous thromboembolic events (VTEs) were collected and graded using the U.S. National Cancer Institute's Common Terminology Criteria for Adverse Events, version 3.0. Time to treatment failure (TTF) and overall survival (OS) were determined using the Kaplan-Meier method. RESULTS: Overall, the 43 study patients received 398 cycles of anticancer therapy (median: 6 cycles; range: 1-24 cycles). No gi perforations were identified. However, 4 bleeding events occurred (9.3%), 3 requiring permanent discontinuation of bevacizumab. Also, 6 grade 3 or 4 VTEs occurred (14.0%), 3 of which required a hospital admission. In addition, grades 3 and 4 diarrhea, febrile neutropenia, and proteinuria showed cumulative incidences of 11.6%, 2.3%, and 2.3% respectively. Median TTF was 6.3 months; median os was 24.4 months. CONCLUSIONS: Bevacizumab in combination with FOLFIRI appears to be well tolerated, and efficacy is consistent with trial reports. However, patients should be closely monitored to avoid potentially serious events such as bleeding and VTEs.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".