Managing Treatment-Related Adverse Events Associated with egfr Tyrosine Kinase Inhibitors in Advanced Non-Small-Cell Lung Cancer
Bibliographic record
Abstract
Non-small-cell lung cancer (nsclc) has the highest prevalence of all types of lung cancer, which is the second most common cancer and the leading cause of cancer-related mortality in Canada. The need for more effective and less toxic treatment options for nsclc has led to the development of agents targeting the epidermal growth factor receptor (egfr)-mediated signalling pathway, such as egfr tyrosine kinase inhibitors (egfr-tkis). Although egfr-tkis are less toxic than traditional anti-neoplastic agents, they are commonly associated with acneiform-like rash and diarrhea. This review summarizes the clinical presentation and causes of egfr-tki-induced rash and diarrhea, and presents strategies for effective assessment, monitoring, and treatment of these adverse effects. Strategies to improve the management of egfr-tki-related adverse events should improve clinical outcomes, compliance, and quality of life in patients with advanced nsclc.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".