Bevacizumab in Combination With Oxaliplatin-Based Chemotherapy As First-Line Therapy in Metastatic Colorectal Cancer: A Randomized Phase III Study
Bibliographic record
Abstract
PURPOSE: To evaluate the efficacy and safety of bevacizumab when added to first-line oxaliplatin-based chemotherapy (either capecitabine plus oxaliplatin [XELOX] or fluorouracil/folinic acid plus oxaliplatin [FOLFOX-4]) in patients with metastatic colorectal cancer (MCRC). PATIENTS AND METHODS: Patients with MCRC were randomly assigned, in a 2 x 2 factorial design, to XELOX versus FOLFOX-4, and then to bevacizumab versus placebo. The primary end point was progression-free survival (PFS). RESULTS: A total of 1,401 patients were randomly assigned in this 2 x 2 analysis. Median progression-free survival (PFS) was 9.4 months in the bevacizumab group and 8.0 months in the placebo group (hazard ratio [HR], 0.83; 97.5% CI, 0.72 to 0.95; P = .0023). Median overall survival was 21.3 months in the bevacizumab group and 19.9 months in the placebo group (HR, 0.89; 97.5% CI, 0.76 to 1.03; P = .077). Response rates were similar in both arms. Analysis of treatment withdrawals showed that, despite protocol allowance of treatment continuation until disease progression, only 29% and 47% of bevacizumab and placebo recipients, respectively, were treated until progression. The toxicity profile of bevacizumab was consistent with that documented in previous trials. CONCLUSION: The addition of bevacizumab to oxaliplatin-based chemotherapy significantly improved PFS in this first-line trial in patients with MCRC. Overall survival differences did not reach statistical significance, and response rate was not improved by the addition of bevacizumab. Treatment continuation until disease progression may be necessary in order to optimize the contribution of bevacizumab to therapy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".