Efficacy and safety of abrocitinib in patients with moderate‐to‐severe atopic dermatitis and comorbid allergies
Bibliographic record
Abstract
BACKGROUND: Abrocitinib efficacy by comorbidity status in patients with moderate-to-severe atopic dermatitis (AD) has not been previously assessed. This post hoc analysis evaluated the efficacy and safety of abrocitinib in patients with AD and allergic comorbidities. METHODS: Data were pooled from patients who received abrocitinib 200 mg, 100 mg, or placebo in phase 2b (NCT02780167) and phase 3 (NCT03349060, NCT03575871) monotherapy trials. Patients with and without allergic comorbidities (allergic asthma, rhinitis, conjunctivitis, or food allergy) were evaluated for Investigator's Global Assessment (IGA) response (clear [0] or almost clear [1]), ≥75% improvement in the Eczema Area and Severity Index (EASI-75), ≥4-point improvement in Peak Pruritus Numerical Rating Scale (PP-NRS4), and Dermatology Life Quality Index (DLQI) response (<2 with baseline score ≥2). Other outcomes were Patient-Oriented Eczema Measure (POEM), SCORing Atopic Dermatitis (SCORAD), Pruritus and Symptoms Assessment for Atopic Dermatitis (PSAAD), and treatment-emergent adverse events (TEAEs). RESULTS: Of 942 patients, 498 (53%) reported at least one allergic comorbidity (asthma only, 33%; conjunctivitis only or rhinitis only or both, 17%; food allergies only, 15%; >1 allergic comorbidity, 34%). Regardless of comorbidity status, from Week 2 to Week 12, higher percentages of patients treated with either abrocitinib dose achieved IGA 0/1, EASI-75, PP-NRS4, or DLQI 0/1 versus placebo-treated patients. Changes from baseline in POEM, SCORAD, and PSAAD were greater with abrocitinib than with placebo in patients with and without allergic comorbidities. Most TEAEs were mild or moderate. CONCLUSIONS: Efficacy and safety data support abrocitinib use to manage AD in patients with or without allergic comorbidities.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".