Management of Common Toxicities in Metastatic NSCLC Related to Anti-Lung Cancer Therapies with EGFR–TKIs
Bibliographic record
Abstract
Tyrosine kinase inhibitors (TKIs) against the epidermal growth factor receptor (EGFR) are the standard of care treatment in non-small cell lung cancer (NSCLC). TKIs are used first line in EGFR mutation-positive NSCLC; erlotinib is the only TKI approved for subsequent lines of treatment in EGFR wild-type NSCLC. As promising as TKIs are in helping patients avoid some of the side effects of traditional cytotoxic chemotherapy, they do come with a variety of side effects. This article will describe the most common adverse events associated with the epidermal EGFR family of TKIs including diarrhea, rash, mucositis, and paronychia. The objective of this paper is to provide simple guidelines to assist oncologists in managing these common toxicities. As patient survival is often directly correlated with successful therapeutic drug delivery, the management of TKI-induced adverse events ensures proper treatment and may avoid discontinuation or reduction of the therapeutic.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".