Treatment patterns and outcomes of acneiform eruptions from anti-epidermal growth factor receptor (EGFR) therapies for metastatic colorectal cancer (MCRC).
Bibliographic record
Abstract
668 Background: Use of anti-EGFR therapies, such as cetuximab (cmab) and panitumumab (pmab), is associated with acneiform eruptions. Because prior research suggests a possible correlation between rash severity and outcomes in unselected patients, concerns remain that prophylactic treatment of rash may interfere with anti-tumor activities of these drugs. Our aims were to: 1) characterize the treatment patterns for rashes due to cmab and pmab; and 2) evaluate if a prophylactic vs. reactive approach to rash management modifies outcomes. Methods: All patients diagnosed with wild-type K-ras MCRC from July 2009 to June 2011 in British Columbia, Canada and prescribed either cmab or pmab were identified. We conducted a detailed retrospective review to describe prophylactic (before rash) and reactive (after rash) use of antibiotics and steroid creams. Using Cox regression, the relationship between rash management and overall survival was characterized. Results: In total, 78 eligible patients were analyzed: median age was 62 years, 65% were male, 27% received cmab and 73% pmab, and median number of anti-EGFR treatment was 9 cycles. Rash occurred in 88% of patients. Among them, reactive treatment was favored over prophylactic treatment (74% vs. 26%). There were no differences in rash management based on any patient or tumor characteristics (all p>0.05). Median overall survival was 7.2 months. The number of treatment cycles and overall survival were similar in both prophylactic and reactive groups. In Cox regression, ECOG 2+ correlated with worse overall survival (HR for death 5.95, 95% CI 1.68- 21.13). However, outcomes were statistically similar between patients prescribed antibiotics prophylactically vs. reactively (HR=1.47, 95% CI 0.37-5.90) and between patients given steroid creams prophylactically vs. reactively (HR=0.75, 95% CI 0.11-4.88). Conclusions: Prophylactic treatment of anti-EGFR related rash is associated with similar outcomes as reactive rash treatment in wild-type K-ras MCRC patients. Because rash can lead to decreased quality of life, pre-emptive skin treatment represents a reasonable strategy for patients on anti-EGFR therapies.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".