Impact of baricitinib in combination with topical steroids on atopic dermatitis symptoms, quality of life and functioning in adult patients with moderate‐to‐severe atopic dermatitis from the BREEZE‐AD7 Phase 3 randomized trial
Bibliographic record
Abstract
BACKGROUND: Baricitinib is an oral, selective, reversible Janus kinase 1/2 inhibitor approved in the European Union and Japan and under investigation in the United States for treatment of atopic dermatitis (AD). OBJECTIVES: To evaluate the impact of baricitinib plus background topical corticosteroids (TCS) on health-related quality of life (HRQoL), how AD symptoms impact work productivity and life functioning, and treatment benefit using patient-reported outcome (PRO) assessments in patients with moderate-to-severe AD previously experiencing inadequate response to TCS. METHODS: Adult patients with AD in BREEZE-AD7, a Phase 3, multicentre, double-blind trial, were randomised 1 : 1 : 1 to daily oral placebo (control) or baricitinib 4- or 2-mg plus TCS. PROs reported Week 1 through Week 16: Dermatology Life Quality Index (DLQI), Work Productivity and Activity Impairment-AD (WPAI-AD); Patient-Reported Outcomes Measurement Information System (PROMIS) Itch and Sleep measures, and Patient Benefit Index (PBI). Data were analysed using logistic regression (categorical) and mixed model repeated measures (continuous). PBI scores were analysed using analysis of variance. RESULTS: A total of 329 patients were randomised. Treatment with baricitinib 4-mg (N = 111) or 2 mg (N = 109) plus TCS led to rapid, statistically significant improvements [vs. TCS plus placebo (N = 109)] in DLQI ≥4-point improvement starting at Week 2 (4-mg plus TCS, P ≤ 0.001; 2-mg plus TCS P ≤ 0.05), change from baseline in WPAI-AD presenteeism at Week 1 (4-mg plus TCS, P ≤ 0.01; 2-mg plus TCS P ≤ 0.05) and PROMIS itch interference at Week 2 (4-mg plus TCS P ≤ 0.01). Improvements were sustained through Week 16 for baricitinib 4-mg. Statistically significant improvements were observed at Week 16 for PBI global score (4-mg plus TCS, P ≤ 0.001; 2-mg plus TCS P ≤ 0.05). CONCLUSIONS: Baricitinib plus TCS vs. placebo plus TCS showed significant improvements in treatment benefit at Week 16 and rapid significant improvements in HRQoL and impact of AD symptoms on work productivity and functioning through 16 weeks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".