Aggregate Response Benefit in Skin Clearance and Itch Reduction with Upadacitinib or Dupilumab in Patients with Moderate-to-Severe Atopic Dermatitis®
Bibliographic record
Abstract
Abstract: Background: In patients with moderate-to-severe atopic Dermatitis® (AD), greater skin clearance and itch reduction are associated with more pronounced improvements in quality of life (QoL). Objective : To characterize the aggregate response benefit with upadacitinib versus dupilumab or placebo in patients with moderate-to-severe AD. Methods : Degree of skin clearance and itch response in 3 phase 3 studies (Heads Up [NCT03738397] and Measure Up 1/2 [integrated; NCT03569293/NCT03607422]) were assessed by the Eczema Area and Severity Index (EASI) and Worst Pruritus Numerical Rating Scale (WP-NRS), respectively, using mutually exclusive categories. The aggregate response benefit with upadacitinib over dupilumab or placebo was determined by summing incremental differences for each EASI or WP-NRS category across the full distribution of patient responses. Results : Comparisons across EASI improvement threshold distributions, EASI severity levels, and WP-NRS categories demonstrated an aggregate response benefit favoring upadacitinib over dupilumab as early as week 4 and continuing at weeks 16 and 24. Similar trends were observed for upadacitinib 15 and 30 mg versus placebo. Conclusions : The aggregate response benefit in skin clearance and itch reduction favored upadacitinib 30 mg over dupilumab and upadacitinib 15 or 30 mg over placebo. These benefits may translate to overall greater improvements in patient QoL.
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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 | 0.001 |
| 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) | 0.003 | 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".