Comparison of gefitinib and docetaxel in patients with pretreated advanced non-small cell lung cancer (NSCLC): Meta-analysis from four clinical trials
Bibliographic record
Abstract
8011 Background: Four open-label, randomized trials evaluated gefitinib (G) vs docetaxel (D) in unselected patients with pre-treated advanced NSCLC: INTEREST (Lancet 2008; 372: 1809), V-15–32 (J Clin Oncol 2008; 26: 4244), ISTANA (J Clin Oncol 2008; 26: Abs 8025), SIGN (Anticancer Drugs 2006; 17: 401). Reported here is a meta-analysis using the patient data from each of these trials. Methods: The meta-analysis compared efficacy of G (250 mg/day) and D (75 mg/m2 [V-15–32 60 mg/m2]) using appropriate analysis populations from INTEREST (1466 patients randomized), V-15–32 (489), ISTANA (161) and SIGN (141). Meta-analyses (unadjusted and adjusted for covariates) were performed by Cox proportional hazards for OS and PFS, and by logistic regression for ORR. Results: G demonstrated similar OS and PFS and superior ORR to D in the primary analyses. Secondary analyses demonstrated similar results. Conclusions: Results were consistent with those of the individual studies. Given the similar/superior efficacy demonstrated by G, its favorable tolerability profile, quality of life benefits and oral administration, G has a favorable benefit-risk profile compared with D in a broad pre-treated advanced NSCLC patient population. [Table: see text] [Table: see text]
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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 | 0.020 | 0.022 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.051 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".