Effects of Ethnicity on Outcomes of Patients With EGFR Mutation–Positive NSCLC Treated With EGFR Tyrosine Kinase Inhibitors and Surgical Resection
Bibliographic record
Abstract
Introduction In addition to the higher prevalence of EGFR mutations found among lung cancer cases in East Asian patients, it is unclear whether there are differences in treatment outcomes by ethnicity—that is, East Asian versus non–East Asian. Methods Patients diagnosed with EGFR-mutant lung cancer between January 2004 and October 2014 at a single center were reviewed. Data captured included demographics, tumor and treatment information, and survival. Survival of patients of East Asian and non–East Asian ancestry was compared, including in the subgroup that received EGFR tyrosine kinase inhibitor (TKI) for advanced disease and in those with early-stage disease that underwent surgical resection. Results A total of 348 patients with EGFR-mutant NSCLC were identified. There was a higher proportion of nonsmokers among those of East Asian ethnicity. No significant difference in survival was seen between patients of East Asian and non–East Asian ethnicity, median 6.7 years (95% confidence interval [CI]: 5.4–not applicable) and 5.4 years (95% CI: 4.1–7.2), respectively ( p = 0.09). Among 196 patients that received treatment with EGFR TKI, the median survival from TKI initiation was also similar for those of East Asian and non–East Asian ethnicity, 3.0 years (95% CI: 2.1–3.5) and 2.7 years (95% CI: 2.2–3.5), respectively. Among the early-stage patients that underwent surgical resection (n = 163), those of East Asian ethnicity had similar median recurrence-free survival from surgery compared with non–East Asian patients, 5.3 years (95% CI: 3.5–not applicable) and 5.1 years (95% CI: 3.3–7.2), respectively. Conclusions In a cohort of patients with EGFR-mutant lung cancer with access to uniform standards of care, East Asian ethnicity was not associated with improved survival after treatment with EGFR TKI or surgical resection.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".