Clinical impact of mutation fraction in epidermal growth factor receptor mutation positive NSCLC patients
Bibliographic record
Abstract
BACKGROUND: We examined clinical outcomes in a population-based cohort of EGFR mutant advanced NSCLC patients, exploring the potential role of factors including tumour EGFR mutation fraction and cellularity in predicting outcomes. METHODS: A cohort of patients with EGFR mutant advanced NSCLC was identified (N =2 93); clinical outcomes, pathologic and treatment details were collected. Tumour response was determined from radiology and clinical notes. Association between demographic and pathologic variables EGFR TKI response, time to treatment failure (TTF) and overall survival (OS) was examined using logistic regression and proportional hazards regression. EGFR TKI response rates were summarised by percent mutation fraction to explore their association. RESULTS: Higher mutation fraction was associated with greater EGFR TKI response rate (odds ratio 1.58, 95% CI = 1.21-2.07, P = 0.0008), longer TTF (hazard ratio 0.80, 95% CI = 0.68-0.92, P = 0.003) and better OS (hazard ratio 0.81, 95% CI = 0.67-0.99, P = 0.04). However, even in patients with ⩽ 5% mutation fraction, response rate was 34%. Females had longer TTF (P = 0.02). CONCLUSIONS: EGFR mutation fraction in tumour samples was significantly associated with response, TTF and OS. Despite this, no lower level of mutation fraction was detected for which EGFR TKI should be withheld in those with activating EGFR mutations.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".