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 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 | 0.032 | 0.065 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".