Short-Term Efficacy and Safety of Abrocitinib by Baseline Disease Severity in Patients With Moderate-to-Severe Atopic Dermatitis
Bibliographic record
Abstract
BACKGROUND• Atopic dermatitis (AD) is a chronic inflammatory skin disease associated with substantial patient burden that increases with greater disease severity 1,2-Despite treatment with systemic therapies, patients with moderate or severe AD often report significantly worse outcomes than patients with mild AD, including severe itch, pain, and greater impact on quality of life• There is a need for more effective therapies in patients with moderate or severe disease• Abrocitinib is an oral, once-daily, Janus kinase 1-selective inhibitor approved for the treatment of adults and adolescents with moderate-to-severe AD 3-5• Abrocitinib was efficacious and well tolerated in patients when administered as monotherapy or in combination with background topical therapy in multiple phase 3 clinical trials 6-8 OBJECTIVE• To evaluate the efficacy and safety of abrocitinib in patients with moderate-to-severe AD classified by baseline disease severity METHODS Study Design and Assessments• Data were analyzed post hoc from clinical trials with abrocitinib administered as monotherapy (pooled phase 2b [NCT02780167] and phase 3 JADE MONO-1 [NCT03349060] and JADE MONO-2 [NCT03575871]) or in combination with topical therapy (JADE COMPARE; NCT03720470)• The study designs and assessments are shown in Figure 1
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".