Magnitude and Time Course of Response to Abrocitinib for Moderate-to-Severe Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND: Emerging treatments for moderate-to-severe atopic dermatitis (AD) may provide greater and faster improvement in AD signs and symptoms than current therapies. OBJECTIVE: To examine JADE COMPARE (NCT03720470) data using stringent efficacy end points. METHODS: Adults with moderate-to-severe AD were randomly assigned 2:2:2:1 to receive oral abrocitinib 200 or 100 mg once daily, subcutaneous dupilumab 300 mg every 2 weeks (600-mg loading dose), or placebo, with medicated topical therapy for 16 weeks. Stringent response thresholds were applied for Eczema Area and Severity Index (EASI), Investigator's Global Assessment, Dermatology Life Quality Index, Peak Pruritus Numerical Rating Scale, and Night Time Itch Scale severity. RESULTS: At week 16, 48.9%, 38.0%, and 38.8% of the abrocitinib 200-mg, 100-mg, and dupilumab groups, respectively, achieved greater than or equal to 90% improvement from baseline in EASI versus 11.3% placebo; 14.9%, 12.6%, and 6.5% achieved Investigator's Global Assessment 0 (clear) versus 4.8% placebo; 29.7%, 21.6%, and 24.0% achieved Dermatology Life Quality Index 0/1 (no/minimal impact on quality of life) versus 10.6% placebo; and 57.1%, 44.5%, and 46.1% achieved Night Time Itch Scale severity 0/1 (no/minimal night-time itch) versus 31.9% placebo. Kaplan-Meier median time to greater than or equal to 90% improvement from baseline in EASI was 59, 113, and 114 days in the abrocitinib 200-mg, 100-mg, and dupilumab groups, respectively, and was not evaluable for placebo; median time to Peak Pruritus Numerical Rating Scale 0/1 (no/very minimal itch) was 86 and 116 days for abrocitinib 200-mg and dupilumab groups, respectively, and was not evaluable for abrocitinib 100-mg and placebo groups. CONCLUSIONS: A greater proportion of patients treated with abrocitinib than placebo had almost complete control of AD signs and symptoms.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".