Drug-Induced Acne in Inflammatory Bowel Disease: A Practical Guide for the Gastroenterologist
Bibliographic record
Abstract
Drug-induced acne is a common side effect to a wide array of pharmacological therapies and is characterized by a monomorphic, papulopustular eruption typically affecting the face, scalp, and the upper thorax. Corticosteroids and Janus kinase inhibitors (JAKi) are commonly used for the treatment of inflammatory bowel disease (IBD) and are known to aggravate a prior tendency to acne or trigger the development of new acneiform eruptions. Recent attention on managing drug-induced acne has been driven by the increasing use of JAKi, an expanding therapeutic class in IBD and several other immune-mediated inflammatory diseases. Both randomized controlled trials and real-world studies have identified acne as one of the most common treatment-emergent adverse events in JAKi. Left untreated, this common skin reaction can significantly affect patient self-esteem and quality of life leading to poor treatment adherence and suboptimal IBD control. This review examines the characteristics of drug-induced acne in IBD treatments, provides a practical guide for gastroenterologists to manage mild-to-moderate occurrences, and highlights when to seek specialist dermatology advice. Such approaches enable early treatment of a common and often distressing adverse event and optimize the management of IBD by preventing the premature discontinuation or dose reduction of efficacious IBD drugs.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.009 |
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".