Bibliographic record
Abstract
BACKGROUND: Atopic dermatitis (AD) patients have sensitive skin with impaired barrier function. Lyocell is a cellulosic fiber that offers unique characteristics and may be suitable for use by AD patients. OBJECTIVE: To compare preferences of subjects with atopic dermatitis and normal skin for 100% lyocell clothing and bedding versus 100% cotton fabrics. METHODS: Thirty subjects were enrolled and randomly selected to use cotton or lyocell shirts, pajamas, and bedding for 1 week. Following a 1-week washout period, participants wore the other fabric for 1 week. At the end of each week, participants completed a preference questionnaire, and AD subjects also rated daily itching on a visual analog scale. A random subset of AD and normal participants underwent measurement of transepidermal water loss (TEWL). RESULTS: Overall, there was a significant preference for lyocell (vs cotton) for its softness, temperature control, moisture control, and wrinkle resistance. AD subjects did not have stronger fabric preferences than did normal subjects. Although not significant, lower average itching and decreased TEWL were seen in participants while they wore lyocell. CONCLUSION: Lyocell is superior to cotton in many performance characteristics and equivalent to cotton for itch reduction. Lyocell is currently available as a beneficial fabric to improve patient comfort.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".