Characterization of the Population, Treatment Patterns, and Outcomes of Patients with Advanced or Metastatic Non-Small-Cell Lung Cancer (NSCLC) with Epidermal Growth Factor Receptor Mutation (EGFRm): A Retrospective Cohort Study from IPO Porto
Bibliographic record
Abstract
Most patients with non-small-cell lung cancer (NSCLC) present with advanced/metastatic disease at diagnosis, and molecular profiling is critical in guiding treatment decisions. This retrospective cohort study aimed to characterize EGFR mutations (EGFRm) in advanced/metastatic NSCLC patients, treatment patterns, and real-world outcomes. Adults diagnosed between 2018 and 2021 and treated at a Comprehensive Care Center were included. Time-to-event outcomes were analyzed using the Kaplan–Meier method. A total of 110 patients were included, with a median age of 69.0 years (range, 37–93), 76.4% female, and 83.2% non-smokers. About 97.3% had adenocarcinomas, with 93.6% at stage IV, 40.9% with ≥ three metastatic sites (brain metastases in 24.5%), 33.6% ECOG 2–4, and 58.2% with an EGFR exon-19 deletion. A minority started supportive care or curative-intent treatment, and 81.8% underwent first-line palliative systemic therapy (TKIs, 91.1%; chemotherapy, 8.9%). Median real-world overall survival (rwOS) was 18.9 months (95% CI, 13.8–28.1). Worse rwOS was observed in patients with ECOG 2–4 versus ECOG 0–1 (10.3 vs. 22.8 months; HR 1.82, 95% CI 1.17–2.85; p = 0.008) and in patients with exon-21 L858R versus exon 19 deletions (15.8 vs. 24.2 months; HR 1.59, 95% CI 1.00–2.54; p = 0.048). In patients treated with palliative systemic treatment, median progression-free survival was 10.9 months (95% CI, 8.8–13.6). This study provides important insights regarding real-world characteristics, treatment patterns, and outcomes from a cohort of EGFRm advanced/metastatic NSCLC patients.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".