Real-World Treatment Patterns, Health Outcomes, and Healthcare Resource Use in Advanced Common EGFR-Positive Non-Small Cell Lung Cancer Patients Treated with Osimertinib in Alberta
Bibliographic record
Abstract
There is limited information on the treatment trajectory and outcomes of patients with advanced cEGFRm NSCLC treated with osimertinib in routine clinical practice in Canada. By using and analyzing population-based administrative data and detailed chart abstraction in the province of Alberta, our objective was to capture Canadian-specific real-world treatment patterns, health outcomes, and healthcare resource utilization (HCRU) in advanced cEGFRm NSCLC patients who were (a) treated with osimertinib and (b) those receiving treatment after osimertinib. In our study cohort, we found that the overall survival rates for real-world patients receiving osimertinib were less favorable than those observed in clinical trials (24.0 versus 38.6 months). The attrition rate after osimertinib was substantial and high HCRU persisted across many years after diagnosis and treatment. This study provides important real-world evidence on contemporary survival, treatment patterns, and healthcare use among cEGFRm NSCLC patients treated with osimertinib and suggests that further research efforts are needed to improve therapeutic options in both the first and subsequent line settings.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| 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".