Outcome Differences Between First- and Second-generation EGFR Inhibitors in Advanced EGFR Mutated NSCLC in a Large Population-based Cohort
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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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".