Optimal sequencing strategies in the treatment of EGFR mutation–positive non–small cell lung cancer: Clinical benefits and cost-effectiveness
Bibliographic record
Abstract
PURPOSE: To summarize current understanding of the efficacy, role, and cost-effectiveness of the available epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIs), and to evaluate sequencing strategies based on the available evidence. Summary. EGFR TKIs are the current standard of care for patients with EGFR mutation-positive non-small cell lung cancer (NSCLC). Five EGFR TKIs are currently approved in the United States for use in a first-line setting; these TKIs differ in mechanism of action, efficacy, safety, and cost. Most patients develop resistance to first-line EGFR TKIs and require subsequent therapy with additional EGFR TKIs, chemotherapy, and/or other targeted agents. A major consideration when selecting EGFR TKIs, both as first-line or subsequent treatment options, is cost-effectiveness. Although clinical trials have shown that the second- and third-generation EGFR TKIs are superior in efficacy to the first-generation agents, pharmacoeconomic studies suggest that the first-generation agents are the most cost-effective, with the second-generation TKI afatinib also considered cost-effective in some studies. Despite its impressive efficacy, osimertinib appears to be less cost-effective due to substantially higher acquisition costs. CONCLUSION: Preliminary data suggest that first-line afatinib followed by osimertinib may offer promising survival outcomes and, on the basis of efficacy alone, may represent an optimal sequencing strategy in the majority of patients with EGFR mutation-positive NSCLC, in particular Asian patients and those with Del19-positive tumors. However, considerably more research into outcomes and costs associated with consecutive sequencing of EGFR TKIs is needed before any conclusions can be reached.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".