Real-World Testing Practices, Treatment Patterns and Clinical Outcomes in Patients from Central Eastern Europe with EGFR-Mutated Advanced Non-Small Cell Lung Cancer: A Retrospective Chart Review Study (REFLECT)
Bibliographic record
Abstract
The targeted therapy with tyrosine kinase inhibitors (TKIs) against the epidermal growth factor receptor mutation (EGFRm) in advanced non-small cell lung cancer (NSCLC) changed the treatment paradigm. REFLECT study (NCT04031898) explored EGFR/T790M testing and treatment patterns in EGFRm NSCLC patients receiving first- or second-generation (1G/2G) EGFR TKIs as front-line (1L) in eight countries. Pooled data from Central Eastern Europe (CEE) countries from this study (Bulgaria, Poland, Romania, Slovenia) are presented here. This physician-led chart review study was conducted in patients with confirmed-EGFRm NSCLC initiating 1L 1G/2G EGFR TKIs between 2015–2018. The CEE cohort included 389 patients receiving 1L erlotinib (37%), afatinib (34%), and gefitinib (29%). Overall, 320 (82%) patients discontinued 1L, and 298 (77%) progression events were registered. Median progression free survival on 1L TKIs was 14.0 (95% CI: 12.6–15.6) months. Median overall survival from 1L start was 26.6 (95% CI: 24.1–29.0) months. Attrition rate between 1L and next line was 30%. Among patients with 1L progression, 200 (67%) were tested for T790M and 58% were positive. This first CEE analysis of treatments and outcomes in EGFRm NSCLC patients highlights the importance of using the most efficacious therapies currently available in 1L to reduce attrition and improve patient outcomes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".