Erlotinib as Second-Line Therapy for Patients with Advanced Non-Small-Cell Lung Cancer and Wild-Type EGFR Tumors
Bibliographic record
Abstract
Aim: The objective of the study was to determine the efficacy and safety of erlotinib in second-line therapy for patients with advanced non-small-cell lung carcinoma (NSCLC) and wild-type tumors, measuring progression-free survival (PFS), the response rate, and overall survival (OS). Material and Methods: This retrospective, observational, and multicenter study involved 47 patients diagnosed with NSCLC and wild-type epidermal growth factor receptor(EGFR) who received erlotinib as second-line therapy in four Spanish hospitals. Primary and secondary endpoints included the determination of the efficacy (by measuring progression-free survival, PFS, the response rate, and overall survival, OS) and safety profile of erlotinib. Results: The median PFS was 2.33 months (95% CI, 0.4-10.9). No differences in PFS were found regarding sex, age, smoking habits, ECOG performance status, and tumor histology. The median OS was 4.00 months (95% CI, 1.18-6.82). Four patients developed grade 3-4 non-hematological toxicities, including asthenia, cutaneous toxicity, and renal failure. One patient developed grade 3-4 thrombocytopenia. Conclusion: Our study corroborates the modest but clear benefit of second-line agents, including erlotinib, for the treatment of advanced NSCLC, and supports their administration in patients with wild-type EGFR. Further prospective studies involving large number of patients are required to corroborate such results.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".