EGFR-Targeted Therapy for Non-Small Cell Lung Cancer: Focus on EGFR Oncogenic Mutation
Bibliographic record
Abstract
The two essential requirements for pathologic specimens in the era of personalized therapies for non-small cell lung carcinoma (NSCLC) are accurate subtyping as adenocarcinoma (ADC) versus squamous cell carcinoma (SqCC) and suitability for EGFR molecular testing, as well as for testing of other oncogenes such as EML4-ALK and KRAS. Actually, the value of EGFR expressed in patients with NSCLC in predicting a benefit in terms of survival from treatment with an epidermal growth factor receptor targeted therapy is still in debate, while there is a convincing evidence on the predictive role of the EGFR mutational status with regard to the response to tyrosine kinase inhibitors (TKIs).This is a literature overview on the state-of-the-art of EGFR oncogenic mutation in NSCLC. It is designed to highlight the preclinical rationale driving the molecular footprint assessment, the progressive development of a specific pharmacological treatment and the best method to identify those NSCLC who would most likely benefit from treatment with EGFR-targeted therapy. This is supported by the belief that a rationale for the prioritization of specific regimens based on patient-tailored therapy could be closer than commonly expected.
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) | 0.001 | 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".