Impact of Achieving Optimal Treatment Targets and Minimal Disease Activity on Health-Related Quality of Life and Satisfaction in Patients with Atopic Dermatitis
Bibliographic record
Abstract
INTRODUCTION: Guidance from the Aiming Higher in Eczema/Atopic Dermatitis initiative identified moderate and optimal treatment targets for clinician-reported outcomes (ClinROs) and patient-reported outcomes (PROs) and defined minimal disease activity (MDA) as simultaneously meeting optimal targets in ClinRO and PRO. This post hoc analysis investigates the impact of achieving individual optimal targets or MDA on patient health-related quality of life (HRQoL) outcomes in patients with atopic dermatitis. METHODS: Patients from phase 3 Measure Up 1 (NCT03569293), Measure Up 2 (NCT03607422), and AD UP (NCT03568318) were randomized 1:1:1 to receive daily oral upadacitinib at either 15 mg or 30 mg, or placebo for the first 16 weeks. Patients were pooled for this analysis regardless of intervention and stratified into three mutually exclusive response groups meeting optimal, moderate, or neither treatment target for each ClinRO or PRO, and the achievement of MDA at week 16. Impact on the patient's HRQoL was measured across eight outcomes: itch, skin symptoms, quality of life, sleep, daily activities, emotional state, work productivity, and treatment satisfaction. RESULTS: Patients who achieved optimal treatment targets, compared with those achieving moderate or neither treatment target, reported greater improvement in patient HRQoL outcomes (1.1-20.2-fold for optimal versus moderate, 1.3 to > 50-fold for optimal versus neither target, and 1.2-16.3-fold for moderate versus neither target groups). In addition, patients who achieved MDA, versus those achieving optimal ClinRO or PRO alone, were more likely to report improved patient HRQoL outcomes. CONCLUSIONS: These results highlight the value of reaching optimal treatment targets and MDA in disease management of atopic dermatitis.
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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 | 0.004 | 0.004 |
| 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 | 0.000 |
| 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".