Epidermal growth factor receptor tyrosine kinase inhibitors in non-small cell lung cancer: a decade of progress and hopeful future.
Bibliographic record
Abstract
Nearly 50% of patients with non-small cell lung cancer (NSCLC) are found to have metastatic disease at presentation (1). Platinum doublet chemotherapy remains the standard initial therapy for a vast majority of patients with advanced NSCLC who have a good performance status. Approximately 10% of patients with advanced NSCLC have activating mutations in the epidermal growth factor receptor tyrosine kinase ( EGFR TK) in the tumor tissues (2). Significant progress has been made in the use of molecularly targeted therapies in lung cancer since the initial discovery linking the presence of certain EGFR TK mutations with exquisite responsiveness to EGFR tyrosine kinase inhibitor (TKI) gefitinib (3,4). Although erlotinib, another EGFR TKI, has been approved for use in patients with advanced NSCLC who have progressive disease after platinum based therapy based on the randomized study sponsored by the National Cancer Institute (NCI)-Canada, it is evident now that the impressive clinical benefit from EGFR TKIs is seen almost exclusively in patients whose tumor cells demonstrate specific mutations in the EGFR TK domain (5).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".