A double‐blind study of tolerance and efficacy of a new urea‐containing moisturizer in patients with atopic dermatitis
Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis patients almost all use moisturizers to prevent and treat their skin disease. However, the safety and efficacy of moisturizers are rarely studied in this patient population. Aims To evaluate the efficacy and tolerability of urea-containing moisturizers in subjects with atopic dermatitis. METHODS: One hundred subjects with atopic dermatitis were randomized to apply either a new 5% urea moisturizer or a commercially available 10% urea lotion twice a day for 42 days. Scoring Atopic Dermatitis severity index (SCORAD) was performed at Day 0 and Day 42. Cosmetic acceptability questionnaires, adverse events, and a 5-point tolerance evaluation were administered or performed at Day 42. RESULTS: Both study products were very well tolerated by subjects and only three subjects discontinued their participation in the study due to adverse events. Mean SCORAD significantly decreased between Day 0 and Day 42 by 19.76% and 19.23%, respectively, for subjects treated with the new 5% urea moisturizer or the 10% urea lotion (P < 0.001). There was no difference between the two products in SCORAD reduction; however, significantly more subjects preferred using the new 5% urea moisturizer as compared with the 10% urea lotion. CONCLUSIONS: Both the new 5% urea moisturizer and the 10% urea lotion improved atopic dermatitis and were very well tolerated. However, the cosmetic acceptability questionnaire showed that subjects preferred using the new 5% urea moisturizer over the 10% urea lotion.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".