The impact of population-based EGFR testing in metastatic non-small cell lung cancer in Alberta.
Bibliographic record
Abstract
e21139 Background: While Epidermal Growth Factor Receptor (EGFR) Tyrosine Kinase Inhibitors have been shown to be effective in phase III randomized trials, the value of targeted therapies have been challenging to evaluate at the population level. We examined the impact of population-level EGFR testing and treatment on survival outcomes among metastatic Non-Small Cell Lung Cancer (NSCLC) patients. Methods: Real-world, population-level data were collected from all de novo metastatic non-squamous NSCLC patients in Alberta, Canada from 2004 to 2020. EGFR testing data were collected through Alberta Precision Laboratories using various text mining approaches. Differences in survival rates and overall survival (OS) pre (2004-2012) and post-initiation (post) (2013-2019) testing periods were evaluated using interrupted time series analyses. The impact of testing and subsequent treatment weas evaluated using multivariable Cox Proportional Hazards models. Results: In total, 4,578 metastatic NSCLC patients with a confirmed non-squamous cell carcinoma histology were diagnosed pre- EGFR testing and 4,457 patients were diagnosed post- EGFR testing (2013-2019). Among patients diagnosed in the pre- EGFR testing period, the 6-month, 1-year, and 2-year survival probabilities were 0.39 (95% CI: 0.38-0.41), 0.22 (95% CI: 0.21-0.23), and 0.09 (95% CI: 0.08-0.10), while the survival probabilities for patients diagnosed in the post- EGFR testing period were 0.45 (95% CI: 0.43-0.46), 0.29 (95% CI: 0.27-0.30), and 0.16 (95% CI: 0.15-0.1 7), respectively. After adjusting for baseline patient and clinical characteristics, OS in the post- EGFR period was significantly improved compared to the pre- EGFR period (HR: 0.81; 95% CI: 0.78-0.85). In the post-EGFR period, among patients who were treated with systemic therapy, those tested for an EGFR mutation had significantly greater survival than patients who were not tested HR of 0.81 (95% CI: 0.70-0.95). Conclusions: These results show the considerable impact of population-based molecular testing and subsequent targeted therapies on survival among advanced NSCLC patients. The estimates here can be used in future studies to evaluate the population-level cost-effectiveness of testing and treatment.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".