Randomized Phase II Study of Gefitinib Compared With Placebo in Chemotherapy-Naive Patients With Advanced Non–Small-Cell Lung Cancer and Poor Performance Status
Bibliographic record
Abstract
PURPOSE: To compare gefitinib with placebo in chemotherapy naïve patients with advanced non-small-cell lung cancer (NSCLC) and poor performance status. PATIENTS AND METHODS: NSCLC patients (chemotherapy naïve, WHO performance status 2 or 3; unfit for chemotherapy; stage IIIB/IV) were randomly assigned to gefitinib (250 mg/d) plus best supportive care (BSC; n = 100) or placebo plus BSC (n = 101). The primary end point was progression-free survival (PFS). Secondary end points included overall survival (OS), objective response rate (ORR), quality of life (QOL), pulmonary symptom improvement (PSI), and safety. Correlation of gefitinib efficacy with EGFR gene copy number (fluorescent in situ hybridization [FISH]) was explored. RESULTS: Hazard ratios (HRs; gefitinib:placebo) were 0.82 (95% CI, 0.60 to 1.12; P = .217) for PFS and 0.84 (95% CI, 0.62 to 1.15; P = .272) for OS. As expected for this patient population, OS for both arms was poor, at about 3 months. ORRs were 6.0% (gefitinib) and 1.0% (placebo). QOL and PSI rates were 21.1% and 28.3% (gefitinib) and 20.0% and 28.3% (placebo), respectively. In EGFR FISH-positive patients (n = 32), HRs were 0.29 (95% CI, 0.11 to 0.73) for PFS and 0.44 (95% CI, 0.17 to 1.12) for OS. No unexpected adverse events occurred. CONCLUSION: There was no statistically significant difference in PFS, OS, and ORRs after treatment with gefitinib or placebo, in the overall population; improvements in QOL and symptoms were similar in both groups. Tolerability profile of gefitinib was consistent with previous studies. PFS was statistically significantly improved for gefitinib-treated patients with EGFR FISH-positive tumors.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".