Cediranib with mFOLFOX6 vs bevacizumab with mFOLFOX6 in previously treated metastatic colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Cediranib is a highly potent inhibitor of vascular endothelial growth factor (VEGF) signalling with activity against all three VEGF receptors. Bevacizumab is an anti-VEGF-A monoclonal antibody with clinical benefit in previously treated metastatic colorectal cancer (mCRC). METHODS: Patients with mCRC who had progressed following first-line therapy were randomised 1:1:1 to modified (m)FOLFOX6 plus cediranib (20 or 30 mg day(-1)) or bevacizumab (10 mg kg(-1) every 2 weeks). The primary objective was to compare progression-free survival (PFS) between treatment arms. RESULTS: A total of 210 patients were included in the intent-to-treat (ITT) analysis (cediranib 20 mg, n=71; cediranib 30 mg, n=73; bevacizumab, n=66). Median PFS in the cediranib 20 mg, cediranib 30 mg and bevacizumab groups was 5.8, 7.2 and 7.8 months, respectively. There were no statistically significant differences between treatment arms for PFS (cediranib 20 mg vs bevacizumab: HR=1.28 (95% CI, 0.85-1.95; P=0.29); cediranib 30 mg vs bevacizumab: HR=1.17 (95% CI, 0.77-1.76; P=0.79)) or overall survival (OS). Grade ≥ 3 adverse events were more common with cediranib 30 mg (91.8%) vs cediranib 20 mg (81.4%) or bevacizumab (84.8%). CONCLUSION: There were no statistically significant differences between treatment arms for PFS or OS. When combined with mFOLFOX6, the 20 mg day(-1) dose of cediranib was better tolerated than the 30 mg day(-1) dose.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".