Bevacizumab Plus Interferon Alfa Compared With Interferon Alfa Monotherapy in Patients With Metastatic Renal Cell Carcinoma: CALGB 90206
Bibliographic record
Abstract
PURPOSE: Bevacizumab is an antibody that binds to vascular endothelial growth factor (VEGF) and has activity in metastatic renal cell carcinoma (RCC). Interferon alfa (IFN) is a historic standard first-line treatment for RCC. A prospective, randomized phase III trial of bevacizumab plus IFN versus IFN monotherapy was conducted. PATIENTS AND METHODS: Patients with previously untreated, metastatic clear-cell RCC were randomly assigned to receive either bevacizumab (10 mg/kg intravenously every 2 weeks) plus IFN (9 million U subcutaneously three times weekly) or the same dose and schedule of IFN monotherapy in a multicenter phase III trial. The primary end point was overall survival (OS). Secondary end points were progression-free survival (PFS), objective response rate (ORR), and safety. RESULTS: Between October 2003 and July 2005, 732 patients were enrolled. The prespecified stopping rule for OS has not yet been reached. The median PFS was 8.5 months in patients receiving bevacizumab plus IFN (95% CI, 7.5 to 9.7 months) versus 5.2 months (95% CI, 3.1 to 5.6 months) in patients receiving IFN monotherapy (log-rank P < .0001). The adjusted hazard ratio was 0.71 (95% CI, 0.61 to 0.83; P < .0001). Bevacizumab plus IFN had a higher ORR as compared with IFN (25.5% [95% CI, 20.9% to 30.6%] v 13.1% [95% CI, 9.5% to 17.3%]; P < .0001). Overall toxicity was greater for bevacizumab plus IFN, including significantly more grade 3 hypertension (9% v 0%), anorexia (17% v 8%), fatigue (35% v 28%), and proteinuria (13% v 0%). CONCLUSION: Bevacizumab plus IFN produces a superior PFS and ORR in untreated patients with metastatic RCC as compared with IFN monotherapy. Toxicity is greater in the combination therapy arm.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".