Clinical outcomes in non‐small‐cell lung cancer patients with <i>EGFR</i> mutations: pooled analysis
Bibliographic record
Abstract
Non-small-cell lung cancer (NSCLC) with mutations in the epidermal growth factor receptor (EGFR) is a distinct subgroup of NSCLCs that is particularly responsive to EGFR tyrosine-kinase inhibitors (TKIs). A weighted pooled analysis of available studies was performed to evaluate clinical outcome in patients with EGFR-mutated NSCLC who were treated with chemotherapy or EGFR TKIs. Median progression-free survival (PFS) times were pooled from prospective or retrospective studies that evaluated chemotherapy or single-agent EGFR TKIs (erlotinib or gefitinib) in patients with NSCLC and EGFR mutations. Among the studies identified for inclusion in the analysis, 12 evaluated erlotinib (365 patients), 39 evaluated gefitinib (1069 patients) and 9 evaluated chemotherapy (375 patients). Across all studies, the most common EGFR mutations were deletions in exon 19 and the L858R substitution in exon 21. In the weighted pooled analysis, the overall median PFS was 13.2 months with erlotinib, 9.8 months with gefitinib and 5.9 months with chemotherapy. Using a two-sided permutation, erlotinib and gefitinib produced a longer median PFS versus chemotherapy, both individually (P= 0.000 and P= 0.002, respectively) and as a combined group (EGFR TKI versus chemotherapy, P= 0.000). EGFR TKIs appear to be the most effective treatment for patients with advanced EGFR-mutant NSCLC. Ongoing prospective trials comparing the efficacy of first-line chemotherapy and EGFR TKIs in EGFR-mutant disease should provide further insight into the most appropriate way to treat this specific group of patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".