The Effects of Common Over-the-Counter Moisturizers on Skin Barrier Function: A Randomized, Observer-Blind, Within-Patient, Controlled Study
Bibliographic record
Abstract
BACKGROUND: Moisturizers possibly improve atopic dermatitis (AD) by restoration of skin barrier, although some have detrimental effects. OBJECTIVE: The aim of the study was to estimate the effects of several routine moisturizers on barrier functions. METHODS: This is a randomized, forearm-controlled, observer-blind study. Patients older than 12 years with clear to moderate AD were randomized to 1 of 4 moisturizers (Cetaphil Cream, Aveeno Eczema Therapy Moisturizing Cream, CeraVe Moisturizing Cream, Vaseline) applied to nonlesional skin of 1 forearm and no moisturizer to the opposite forearm for 4 weeks. Transepidermal water loss (TEWL), capacitance, pH, and TEWL after tape stripping were evaluated at weeks 0 and 4. In addition, participants without AD underwent baseline measurements only. RESULTS: Twenty patients with AD completed the study. Baseline measurements between the AD group and 10 non-AD controls were similar. After the intervention (AD group), mean TEWL improved in the treated forearm and worsened in the untreated one, but the difference was not significant. There was no significant change in pH or in TEWL after tape stripping. Capacitance significantly improved in the moisturizer forearm. The study was underpowered as recruitment fell short. CONCLUSIONS: The effects of moisturizers on nonlesional AD skin were small and need to be addressed when powering future studies. Broadening investigations beyond the classic barrier properties might be useful in future studies.
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 | 0.008 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".