Baricitinib improves symptoms in patients with moderate-to-severe atopic dermatitis and inadequate response to topical corticosteroids: patient-reported outcomes from two randomized monotherapy phase III trials
Bibliographic record
Abstract
BACKGROUND: Itch, skin pain, and sleep disturbance are burdensome symptoms in atopic dermatitis (AD) that negatively influence a patient's quality of life (QoL). OBJECTIVE: To evaluate the impact of baricitinib on patient-reported outcomes (PROs) in adult patients with moderate-to-severe AD, and explore the association between improvement in key signs and symptoms of AD with improvements in QoL and patient's assessment of disease severity. METHODS: Data were analyzed from two phase III monotherapy trials (BREEZE-AD1/BREEZE-AD2) in which patients were randomized 2:1:1:1 to once-daily placebo, baricitinib 1-mg, 2-mg, or 4-mg for 16 weeks and assessed using PRO measures. RESULTS: ≤.001). Baricitinib significantly reduced SCORing AD (SCORAD) pruritus (4-mg in BREEZE-AD1 and 2-mg in BREEZE-AD2) and Patient Oriented Eczema Measure (POEM) itch (both doses). Improvements in skin pain severity and sleep disturbance were also observed. Improvements in AD symptoms showed higher correlations with patients' assessment of AD severity and QoL than improvements in skin inflammation. CONCLUSIONS: Baricitinib significantly improved symptoms in patients with moderate-to-severe AD. CLINICALTRIALS.GOV IDENTIFIERS: NCT03334396 (BREEZE-AD1) and NCT03334422 (BREEZE-AD2).
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".