Outcomes in patients with aggressive or refractory disease from REVEL: A randomized phase III study of docetaxel with ramucirumab or placebo for second-line treatment of stage IV non-small-cell lung cancer
Bibliographic record
Abstract
OBJECTIVES: The REVEL study demonstrated improved efficacy for patients with advanced non-small cell lung cancer treated with ramucirumab plus docetaxel, independent of histology. This exploratory analysis characterized the treatment effect in REVEL patients who were refractory to prior first-line treatment. MATERIALS AND METHODS: Refractory patients had a best response of progressive disease to first-line treatment. Endpoints included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), quality of life (QoL), and safety. Kaplan-Meier and Cox proportional hazards regression were performed for OS and PFS, and Cochran-Mantel-Haenszel test was used for response. QoL was assessed with the Lung Cancer Symptom Scale. Sensitivity analyses were performed on subgroups of the intent-to-treat population with limited time on first-line therapy. RESULTS: Of 1253 randomized patients in REVEL, 360 (29%) were refractory to first-line treatment. Baseline characteristics were largely balanced between treatment arms. In the control arm, median OS for refractory patients was 6.3 versus 10.3 months for patients not meeting this criterion, demonstrating the poor prognosis of refractory patients. Median OS (8.3 vs. 6.3 months; HR, 0.86; 95% CI, 0.68-1.08), median PFS (4.0 vs. 2.5 months; HR, 0.71; 95% CI, 0.57-0.88), and ORR (22.5% vs. 12.6%) were improved in refractory patients treated with ramucirumab compared to placebo, without new safety concerns or further deteriorating patient QoL. CONCLUSIONS: The effect of ramucirumab in refractory patients is similar to that in the intent-to-treat population. The benefit/risk profile for refractory patients suggests that ramucirumab plus docetaxel is an appropriate treatment option even in this difficult-to-treat population.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".