Treatment of Hailey-Hailey disease with the Janus kinase inhibitor abrocitinib: A case report
Bibliographic record
Abstract
Hailey-Hailey disease is a rare, chronic, autosomal dominant skin disorder characterized by recurrent painful erosions and macerated plaques, primarily affecting intertriginous areas. It is caused by mutations in the ATP2C1 gene, leading to impaired calcium homeostasis and keratinocyte adhesion. Many patients experience poor disease control despite conventional therapies. We report a case of a female in her 60s with refractory Hailey-Hailey disease affecting the perianal, inguinal, and cervical folds, with painful, eroded plaques resistant to conventional treatments. Despite multiple failed therapies, including methotrexate, dapsone, acitretin, and naltrexone, she showed rapid improvement within 2 weeks of abrocitinib (100 mg daily), a JAK1 inhibitor, with sustained control at 2 months follow-up. JAK inhibitors, initially approved for inflammatory diseases such as atopic dermatitis, are emerging as promising therapies for genodermatoses. By suppressing IL-4/IL-13-driven inflammation, JAK1 inhibition may restore epithelial integrity and reduce chronic skin inflammation. This case adds to growing evidence that JAK inhibitors, particularly abrocitinib, may serve as an effective targeted therapy for refractory Hailey-Hailey disease. Further clinical trials are needed to confirm its long-term efficacy and safety.
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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) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".