Itch improvement has a major and comparable effect on the Dermatology Life Quality Index in psoriasis and atopic dermatitis patients
Bibliographic record
Abstract
Abstract Background Itch is known to have a particularly high impact on psoriasis (PsO) and atopic dermatitis (AD) patients' quality of life. Although AD therapies have exhibited a high efficacy when it comes to itch control, itch control with PsO therapies is not as well documented. Objectives The aim of this post‐hoc analysis is to better understand the impact of itch on the patients' quality of life in PsO as well as AD by providing a pairwise correlation between itch improvement and patients' quality of life and determine the predictive factors in patients achieving Dermatology Life Quality Index score of 0 or 1 [DLQI (0/1)]. Methods Three phase III clinical studies, one in PsO and two in AD, were assessed. Pairwise correlations between objective improvement of visible signs of disease, quality of life, and itch intensity were investigated at 16 weeks of treatment. Predictive analyses methods were applied on the data to assess the impact of clinical and itch improvement on the DLQI improvement. Results This study shows that change in itch from baseline in AD and PsO patients correlate to change in DLQI from baseline. Change in itch from baseline was found to be the most important factor in predicting DLQI (0/1). Conclusions These results highlight the necessity to study itch in both PsO and AD clinical trials, and it is recommended that itch may be considered a coprimary or at minimum a secondary efficacy end‐point in all such clinical studies.
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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 | 0.001 | 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) | 0.003 | 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".