Irreversible Tyrosine Kinase Inhibition of Epidermal Growth Factor Receptor with Afatinib in Egfr Activating Mutation–Positive Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Despite recent advances in the systemic therapy of non-small-cell lung cancer (NSCLC), the prognosis for stage IV disease remains poor. The discovery of targetable mutations has led to new treatment options. The most common mutations, the EGFR activating mutations, are present in about 50% of Asian patients and up to 15% of white patients. First-generation reversible epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKIS) have led to improved survival in patients positive for EGFR activating mutations, but resistance eventually leads to disease progression. The irreversible EGFR TKI afatinib was developed to counter such resistance. The clinical efficacy of afatinib has been shown in first-line studies comparing it with both cytotoxic chemotherapy and first-generation EGFR TKIS. Afatinib has also shown continued benefit beyond progression while a patient is taking an EGFR inhibitor. Furthermore, its toxicity profile is both predictable and manageable. The results of the principal clinical trials assessing afatinib are reviewed here.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".