Mean overall survival gain with aflibercept plus FOLFIRI vs placebo plus FOLFIRI in patients with previously treated metastatic colorectal cancer
Bibliographic record
Abstract
BACKGROUND: Mean survival in cancer trials can be estimated with statistical techniques to extrapolate study survival curves. This methodology was applied to data from the VELOUR trial, where use of the novel biologic aflibercept (ziv-aflibercept in the United States) in combination with fluorouracil+leucovorin+irinotecan (FOLFIRI), had significantly increased median overall survival (OS) by 1.44 months, vs placebo plus FOLFIRI in patients with metastatic colorectal cancer (mCRC) resistant to, or that had progressed following, an oxaliplatin-containing regimen. METHODS: Parametric survival analyses were used to identify distributions with the best fit to the empirical VELOUR data. Mean OS for the two treatment groups (and pre-defined subgroups) was calculated from the fitted curves over a 15-year survival period. RESULTS: Overall, the log-logistic distribution was the best-fitting for both treatment arms and, with it, the estimated difference in mean OS over 15 years between aflibercept+FOLFIRI and placebo+FOLFIRI was 4.7 months. In addition, the survival advantage with aflibercept was at least 3 months for the ITT population, whichever distribution was used to extrapolate survival. CONCLUSION: Extrapolation of survival curves suggests the mean OS difference for aflibercept in the VELOUR trial is at least 3 months in the ITT population and selected subgroups.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".