Outcome Differences Between First- and Second-generation EGFR Inhibitors in Advanced EGFR Mutated NSCLC in a Large Population-based Cohort
Why this work is in the frame
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Bibliographic record
Abstract
INTRODUCTION: Second-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs) appear superior to first-generation TKIs in clinical trials, but at the cost of greater toxicity. It is unclear whether real-world patients, who often suffer worse outcomes, experience similar survival benefits. Using population-based data, we aim to characterize outcome differences by type of treatment. PATIENTS AND METHODS: We reviewed all patients with advanced non-small-cell lung cancer who initiated treatment with an EGFR TKI at BC Cancer between 2010 and 2015. A propensity score was generated to account for imbalances in patient characteristics between treatment groups. A Cox proportional hazards model based on the propensity score was then used to estimate effects of treatment on survival. RESULTS: A total of 484 patients were identified for analysis. Patients in the second-generation cohort were younger (62 vs. 67 years), had less baseline central nervous system metastases (9% vs. 22%), and more uncommon EGFR mutations (13% vs. 7%). Patients receiving a second-generation TKI had an improved overall survival (hazard ratio, 0.69; P = .05), driven by the subgroup with an EGFR exon 19 deletion. Patients with a L858R mutation did not appear to derive benefit from a second-generation TKI (hazard ratio, 0.91; P = .74). Overall, 40% of patients receiving a second-generation TKI required a dose reduction, but only 1% required discontinuation. CONCLUSIONS: Second-generation TKIs tended to be chosen over first-generation TKIs as frontline therapy in younger patients with uncommon EGFR mutations and without central nervous system metastases. The survival benefit of a second-generation TKI seen in clinical trials appeared to be generalizable to real-world patients and is a reasonable first-line therapy.
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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) | 0.001 | 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) | 0.000 | 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 it