Associations between Shower and Moisturizing Practices with Atopic Dermatitis Severity: A Prospective Longitudinal Cohort Study
Bibliographic record
Abstract
Background: Evidence-based recommendations for optimal showering/bathing practices are lacking for atopic dermatitis (AD) patients. Objective: To determine longitudinal associations between showering/bathing practices and AD severity in AD patients. Methods: A prospective single-center dermatology practice-based study was performed. Shower/bath frequency and duration, and frequency of applying moisturizers after showering/bathing were evaluated. AD severity was assessed using objective component of Scoring Atopic Dermatitis (o-SCORAD), SCORAD-itch, Eczema Area and Severity Index (EASI), Patient-Oriented Eczema Measure (POEM), and Dermatology Life Quality Index (DLQI). Repeated-measures regression models examined associations of showering/bathing and moisturizing practices with change in AD severity measures over time. Results: Showering/bathing more than daily versus once daily was associated with higher SCORAD-itch, o-SCORAD, EASI, POEM, and DLQI scores; less than daily versus once daily showering/bathing was not associated with any outcomes. Consistent and even inconsistent application of moisturizer after showering/bathing was associated with lower o-SCORAD, EASI, and POEM scores. Showering/bathing duration was not associated with AD outcomes. Severe SCORAD-sleep, o-SCORAD, EASI, and POEM were associated with less adherence to all showering/bathing recommendations. Conclusion: Showering/bathing daily or less frequently and applying moisturizer postshower/bath were associated with lower AD severity; showering/bathing duration was not. Recommendations concerning shower durations may not be necessary when counseling AD patients.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".