Epidermal growth factor receptor tyrosine kinase inhibitors in early-stage nonsmall cell lung cancer
Bibliographic record
Abstract
PURPOSE OF REVIEW: Targeted molecular therapy is playing an increasingly important role in the treatment of nonsmall cell lung cancer (NSCLC). Epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs) have demonstrated efficacy in the advanced disease setting. Preliminary findings suggest that EGFR-TKIs may also be beneficial as adjuvant therapy following complete resection in patients with EGFR-mutation-positive early-stage I-III NSCLC; however, many questions remain unanswered. RECENT FINDINGS: Single-arm trials of adjuvant EGFR-TKI therapy in patients with tumors harboring activating EGFR mutations show impressive 2-year disease-free survival (DFS). Phase III randomized trial data do not support adjuvant EGFR-TKI therapy in unselected completely resected stage I-III NSCLC, but show improved DFS in patients with completely resected EGFR-mutated NSCLC. Adverse events leading to treatment withdrawal and dose reductions are frequent with adjuvant EGFR-TKI therapy, and relapse following treatment withdrawal is common. Adjuvant EGFR-TKIs have not yet been shown to improve the overall survival (OS) in patients with tumors harboring activating EGFR mutations. SUMMARY: There are no data to support the use of adjuvant EGFR-TKIs in unselected early-stage NSCLC. Although EGFR-TKIs hold promise as adjuvant therapy in patients whose tumors harbor EGFR mutations, in the absence of definitive data confirming an OS benefit eligible patients should continue to receive adjuvant chemotherapy following complete resection.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".