First-SIGNAL: First-Line Single-Agent Iressa Versus Gemcitabine and Cisplatin Trial in Never-Smokers With Adenocarcinoma of the Lung
Bibliographic record
Abstract
PURPOSE: Gefitinib has shown high response rate and improved progression-free survival (PFS) in never-smokers with lung adenocarcinoma (NSLAs). We compared efficacy of gefitinib with gemcitabine and cisplatin (GP) chemotherapy in this group of patients as first-line therapy. PATIENTS AND METHODS: In this randomized phase III trial, a total of 313 Korean never-smokers with stage IIIB or IV lung adenocarcinoma, Eastern Cooperative Oncology Group performance status 0 to 2, and adequate organ function were randomly assigned to receive either gefitinib (250 mg daily) or GP chemotherapy (gemcitabine 1,250 mg/m(2) on days 1 and 8; cisplatin 80 mg/m(2) on day 1 every 3 weeks, for up to nine courses). The primary objective was to demonstrate better overall survival (OS) for gefitinib compared with GP in chemotherapy-naive NSLAs. RESULTS: Three hundred nine patients were analyzed per protocol (gefitinib arm, n = 159; GP arm, n = 150). Gefitinib did not show better OS compared with GP (hazard ratio [HR], 0.932; 95% CI, 0.716 to 1.213; P = .604; median OS, 22.3 v 22.9 months, respectively). The 1-year PFS rates were 16.7% with gefitinib and 2.8% with GP (HR, 1.198; 95% CI, 0.944 to 1.520). Response rates were 55% with gefitinib and 46% with GP (P = .101). Myelosuppression, renal insufficiency, and fatigue were more common in the GP arm, but skin toxicities and liver dysfunction were more common in the gefitinib arm. Two patients (1.3%) in the gefitinib arm developed interstitial lung disease and died. CONCLUSION: Gefitinib failed to demonstrate superior OS compared with GP as first-line therapy for NSLAs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| 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 | 0.002 |
| 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".