Epidermal Growth Factor Receptor Inhibitor–Associated Cutaneous Toxicities: An Evolving Paradigm in Clinical Management
Bibliographic record
Abstract
Epidermal growth factor receptor inhibitors (EGFRIs) have demonstrated improved overall survival in patients with non-small cell lung cancer, pancreatic cancer, and colorectal cancer; however, their use is associated with dermatologic reactions of varying severity. The similar spectrum of events observed with monoclonal antibodies and tyrosine kinase inhibitors suggests such toxicities are a class effect. While such reactions do not necessarily require any alteration in EGFRI treatment, being best addressed through symptomatic treatment, there is limited evidence on which to base such therapies. In October 2006, at an international and interdisciplinary EGFRI dermatologic toxicity forum, the underlying mechanisms of these toxicities were discussed and commonly used therapeutic interventions were evaluated. Our aim was to reach a current consensus on management strategies. A three-tiered, EGFRI-focused toxicity grading system is suggested for the purposes of therapeutic decision making, and as a framework on which to build a stepwise approach to intervention. This approach to successful management is specifically tailored to accurately categorize dermatologic toxicity associated with EGFRIs, and can be easily applied by all health care professionals. The goal is to maximize quality of life in patients who are being treated with these agents--many of whom will be on these drugs for several months or even years.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".