Severe exacerbation of facial dermatitis with swelling following introduction of abrocitinib in a patient with atopic dermatitis
Bibliographic record
Abstract
BACKGROUND: Abrocitinib, an oral small-molecule Janus kinase 1 (JAK1) inhibitor, has been widely accepted for the treatment of moderate-to-severe atopic dermatitis (AD). Currently there is a paucity of data on the adverse events (AEs) after abrocitinib treatment, especially on rare events such as exacerbation of facial dermatitis, and their causal relationship and subsequent management remains poorly elucidated. CASE PRESENTATION: A 43-year-old female patient with moderate AD received dupilumab after failure of topical treatments. Facial dermatitis persisted and became refractory after dupilumab treatment, and the patient changed treatment to oral abrocitinib. Fifteen hours after the first dose of abrocitinib, she developed exacerbation of facial dermatitis with swelling. The patient was initially diagnosed as abrocitinib-induced hypersensitivity. However, a score of 3 of the Naranjo adverse drug reaction assessment indicates week correlation between abrocitinib therapy and exacerbation of facial dermatitis, and negative results from subsequent drug provocation test further suggests no causal relationship. CONCLUSIONS: The present case report highlights the necessity of careful determination of abrocitinib-induced hypersensitivity, which should not be diagnosed simply based on the time sequence between drug exposure and symptom occurrence. In addition, caution should be exercised for drug withdrawal, especially when confirmative evidence is absent. Drug provocation test can be helpful and effective treatments could be continued unless severe AEs occur.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".