Biomarkers of clinical benefit for anti-epidermal growth factor receptor agents in patients with non-small-cell lung cancer
Bibliographic record
Abstract
Non-small-cell lung cancer (NSCLC) remains by far the major cause of cancer-related death in the Western world in both men and women. The majority of patients will be diagnosed with metastatic disease, and chemotherapy doublets remain the cornerstone of treatment for these patients. However, chemotherapy has a minimal impact on long-term survival and prognosis remains poor for these patients. Further improvement in treatment is likely to require incorporation of novel targeted therapies. Among these agents, inhibitors of the epidermal growth factor receptor (EGFR) have demonstrated significant activity in the first-, second- or third-line treatment of NSCLC. The purpose of current paper is to present the evidence for using several proposed molecular biomarkers as a tool for selection of NSCLC patients for anti-EGFR treatment. According to current data, EGFR mutation status appears to be the strongest predictor for the selection of NSCLC patients to first-line treatment with EGFR tyrosine kinase inhibitors vs chemotherapy. Use of other biomarkers remains investigational.
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 | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".