A systematic review of oral retinoids for treatment of acneiform eruptions induced by epidermal growth factor receptor inhibitors
Bibliographic record
Abstract
Epidermal growth factor receptor inhibitors (EGFRi) are now standard of care in patients with EGFR mutations in non-small cell lung cancer (NSCLC) and are increasingly being used in other EGFR mutated cancers, including gastrointestinal, and head and neck. However, EGFRi are well known to cause acneiform eruptions, which are shown to positively correlate with tumor response to treatment, but may be severe enough to cause interruption of their treatment. Although most guidelines call for the use of tetracyclines to treat these acneiform eruptions, there is mounting evidence for the use of systemic retinoids instead. The objective of this review is to summarize available data on the use of systemic retinoids for management of acneiform eruptions on EGFRi. This study was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. MEDLINE and EMBASE were searched from database inception until December 10th, 2021. All articles were screened and relevant data extracted independently in duplicate by two reviewers. In total, 16 case reports, case series and retrospective reviews were included. Forty-three patients were treated with retinoids for their acneiform eruption due to EGFRi. The majority (77%) noted moderate to significant improvement after treatment initiation with minimal adverse events (16%). The findings of this systematic review suggest that systemic retinoids are a safe and effective therapy for the management of acneiform eruptions induced by EGFRi.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".