Epidermal growth factor receptor inhibitors in the treatment of lung cancer: reality and hopes
Bibliographic record
Abstract
PURPOSE OF REVIEW: Inhibition of the epidermal growth factor receptor (EGFR) is now an established part of the treatment of nonsmall cell lung cancer. This review summarizes the clinical trials that have been performed with monoclonal antibodies and small molecule tyrosine kinase inhibitors targeting EGFR in nonsmall cell lung cancer. RECENT FINDINGS: Erlotinib has established second and third-line efficacy following the BR.21 study. Recently the INTEREST trial showed gefitinib to have an equivalent outcome to docetaxel in the second line setting. Numerous other tyrosine kinase inhibitor drugs and monoclonal antibodies have demonstrated clinical activity in phase I and phase II trials. Novel tyrosine kinase inhibitors may have the ability to overcome resistance to first generation tyrosine kinase inhibitor therapy. Furthermore there are encouraging studies combining EGFR inhibitors and antiangiogenesis drugs such as bevacizumab. SUMMARY: EGFR inhibition, by a range of strategies, remains a central node in the treatment of nonsmall cell lung cancer.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".