Impact of Asian ethnicity on outcome in metastatic EGFR‐mutant non–small cell lung cancer
Bibliographic record
Abstract
Abstract Aim To determine factors associated with survival in de novo stage IV, non–small cell lung cancer (NSCLC) patients possessing epidermal growth factor receptor mutations (EGFRmut + ) receiving tyrosine kinase inhibitors (TKI) in the first‐line setting. Methods The Glans‐Look Lung Cancer Database was used to retrospectively review stage IV EGFRmut + NSCLC patients diagnosed 2010–2016 receiving first‐line TKI. Patients with overall survival times in the upper quartile (≥34 months) were designated “long‐term survivors” (LTS), the remaining deemed “average‐term survivors” and characteristics between these groups were compared in univariate analysis, and multivariable models constructed to determine predictors of outcome. Results Of 170 eligible patients, median overall survival was 21 months. LTS were significantly more likely to be of Asian ethnicity, be never‐smokers and not possess brain or bone metastases at diagnosis. Asian and non‐Asian patients were comparable, save for an increased propensity of Asian patients to be never smokers and have normal‐range BMI. Multivariable analysis revealed Asian ethnicity [hazard ratio (HR) = 0.65; P = 0.016] and never‐smoking history (HR = 0.65; P = 0.034) as indicators of improved outcome, and presence of brain metastasis at diagnosis an indicator of poor outcome (HR = 2.21; P < 0.001). Conclusions Analysis of this population‐based cohort identifies never‐smoking history and absence of brain metastasis along with Asian ethnicity as an independent prognosticators of favorable outcome, and reveals Asian patients to be clinicopathologically similar to non‐Asian patients. These findings suggest Asian patients represent a unique subpopulation within EGFRmut + NSCLC who may possess different biological underpinnings of NSCLC.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| 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 | 0.001 |
| 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 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".