Bibliographic record
Abstract
BACKGROUND: The use of disinfectants is crucial to preventing the spread of nosocomial infections in health care workers. As many as 25 applications of hand disinfectants is a realistic default value during a working day. However, alcohol-based hand disinfectants may weaken skin barrier function and induce dryness and eczema, which decrease their acceptance. OBJECTIVE: To evaluate the effect of ethanol-containing disinfectants with 5% urea on skin barrier function and on sensitivity to an irritant soap (sodium lauryl sulfate [SLS]). METHODS: Twenty healthy volunteers treated one of their forearms twice daily for 17 days with an ethanol-containing gel with 5% urea. Two types of gels with urea were tested. Treatment was randomized to left or right forearm, and the contralateral forearm served as untreated control. Transepidermal water loss, skin capacitance (dryness), and sensitivity to SLS were evaluated. RESULTS: Twice-daily application of the urea-containing ethanol gels lowered transepidermal water loss, prevented dryness, and reduced sensitivity to SLS compared with the untreated control skin. CONCLUSIONS: Improved barrier function using this ethanol gel with urea may have relevance in daily disinfectant procedures.
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 | 0.000 |
| 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) | 0.002 | 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".