EGFR Tyrosine Kinase Inhibitors Prolong Overall Survival in EGFR Mutated Non-Small-Cell Lung Cancer Patients with Postsurgical Recurrence
Bibliographic record
Abstract
It is indisputable that epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) prolong progression-free survival in non-small-cell lung cancer (NSCLC) patients with EGFR mutations better than cytotoxic chemotherapy. However, it is unclear whether EGFR-TKIs prolong the overall survival of NSCLC patients, especially in those with postsurgical recurrence.We retrospectively examined 304 NSCLC patients with known EGFR mutational status (excluding those with squamous cell carcinoma), who had undergone pulmonary resection and experienced postsurgical recurrence. Of these patients, 137 carried EGFR mutations and 167 did not. Of the 137 patients with EGFR mutations, 83 were treated with EGFR-TKIs for their recurrent disease, and 32 of the 167 patients without EGFR mutations were treated with EGFR-TKIs. Postsurgical survival was divided into two parts: recurrence-free survival (RFS) and postrecurrence survival (PRS), and these were compared between the subgroups with a multivariate analysis, adjusted for baseline clinical characteristics.There was no significant difference in RFS between patients with EGFR mutations and those without (P = 0.88). PRS was also similar in these two subgroups when we excluded patients who had been treated with EGFR-TKIs (P = 0.64). In patients with EGFR mutations, PRS was significantly longer in those who were treated with EGFR-TKIs than in those who were not given EGFR-TKIs (P = 0.04). However, among patients without EGFR mutations, PRS was similar in patients who had and had not been treated with EGFR-TKIs (P = 0.87). In conclusion, among NSCLC patients with postsurgical recurrence, EGFR-TKIs prolonged PRS in those with EGFR mutations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".