Effectiveness of a Skin Care Program with a Cream Containing Ceramide C and a Personalized Training for Secondary Prevention of Hand Contact Dermatitis
Bibliographic record
Abstract
Abstract: Background/Objectives: The aim of our study was to investigate the effectiveness of personalized training on skin protection associated with the regular use of ceramide-containing cream (CC) versus other creams (OC) for improving hand contact dermatitis. Methods: We performed a double-center randomized trial that enrolled workers with hand dermatitis. All workers received personalized training. The intervention was 3 times per day application of the study emollient. The control arm used an emollient of choice without ceramide, as needed. The primary outcome was improvement in hand dermatitis at 1 and 3 months of follow-up. Results: In total, 102 patients with hand dermatitis were enrolled in this study. Improvement in dermatitis was found in 40%, 52.5%, 50%, and 63% of OC and CC, at the first and second follow-ups, respectively. The use of CC was significantly associated with an improvement in dermatitis (odds ratios 2.6; 95% confidence intervals 1.30–5.2), analyzed using generalized equation estimation during the follow-up. Conclusion: Our study demonstrated that an educational personalized intervention could improve the signs and symptoms in patients with hand dermatitis, and the use of a CC resulted in a significantly better outcome during the 3 months of follow-up.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".