Gefitinib monotherapy in advanced nonsmall cell lung cancer: a large Western community implementation study
Bibliographic record
Abstract
Epidermal growth factor receptor tyrosine kinase inhibitors represent a new treatment option for patients with advanced nonsmall cell lung cancer (NSCLC). This retrospective study examined to what extent previous clinical trial experience matches large-scale Western community implementation of this treatment. In the Belgian expanded access programme, the data from 513 patients with advanced or metastatic NSCLC, not suitable for further chemotherapy and receiving oral gefitinib 250 mg.day(-1) until disease progression, death or unacceptable toxicity, were analysed. The median (range) duration of gefitinib treatment was 2.3 months (0.0-32.7). Its use was predominantly in second- or third-line treatment. The overall response and disease control rates were 8.9 and 41.2%, respectively. In univariate analysis, response was more common in females and never-smokers. In multivariate analysis, female sex was the only significant predictive factor (odds ratio (OR) (95% confidence interval (CI)) 0.329 (0.129-0.839)). Symptom improvement was reported in 108 patients of whom 32 (29.6%) had an objective response, 66 (61.1%) experienced disease stabilisation and 10 (9.3%) progressed. Gefitinib was well tolerated; only 7.8% of the patients reported grade 3 or 4 toxicity. The overall median survival was 4.7 months, with a 1-yr survival rate of 21%. Survival was strongly influenced by a better performance status (PS) (good PS: hazard ratio (HR) (95%CI) 0.110 (0.077-0.157)) and adenocarcinoma with bronchioloalveolar carcinoma features histology (HR (95%CI) 0.483 (0.279-0.834)). In conclusion, the activity of gefitinib was confirmed in the present large Western community implementation study. Response, present in a small subgroup, led to a rewarding survival and could be predicted by sex only. Baseline performance status and adenocarcinoma with bronchioloalveolar carcinoma features histology were significant factors for survival.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".