Real-world evidence on the benefits of optimal itch relief and skin clearance in atopic dermatitis management: a study from the TARGET-DERM AD registry
Bibliographic record
Abstract
In atopic dermatitis (AD), the real-world impact of achieving itch and skin lesion treatment targets compared to partial improvement remains unclear. We assessed the relationship between itch relief (reduction in Worst Itch Numeric Rating Scale [WI-NRS]) and skin clearance (Investigator Global Assessment [IGA] 0/1) with other patient-reported outcomes. Using TARGET-DERM AD registry data on adults receiving standard-of-care treatment, we described and modeled the relationship of itch severity (Worst Itch Numeric Rating Scale [WI-NRS]) and skin lesion severity (IGA) outcomes with patient-reported (quality of life ([DLQI)], AD severity [(POEM]), sleep ([Sleep-NRS]), and skin pain [(Pain-NRS]). Among 1,920 participants (58.6% female; 54.5% Non-Hispanic White; 93.8% US; mean age 45 years), ideal outcomes (DLQI 0/1, POEM 0-2, Sleep-NRS 0/1, and Pain-NRS 0/1) were most frequent for those achieving the optimal targets for itch (WI-NRS 0/1; 52.1%, 53.7%, 57.3%, and 83.1%, respectively) and skin clearance (IGA 0/1; 44.7%, 44.3%, 44.7%, and 74.3%, respectively). The odds ratios of ideal outcomes were greatest for participants with complete or near-complete resolution of both itch and skin (DLQI 0/120.0; POEM 0-2: 41.7; Sleep-NRS: 16.1; Pain-NRS: 6.0). Achieving optimal treatment targets for both itch and skin lesion improvement markedly enhances patient-reported AD outcomes. The results of this study support using minimal disease activity criteria to assess therapeutic effectiveness.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".