In vitro methods for evaluating skin hydration under diapers and incontinence products
Bibliographic record
Abstract
BACKGROUND/PURPOSE: Excessive skin hydration from wearing wet undergarments, such as infant diapers and adult incontinence products, has been historically problematic. Skin damage occurs from wetness (urine) and limited product breathability. Evaporative water loss has been measured on adult arms (armband method) or infant torsos (on-baby method), after wearing a saline-insulted diaper product. The current study developed a reliable in vitro method of evaluating diaper and incontinence products for improvements in skin dryness. METHODS: A simulated skin substrate was applied to a heated mechanical arm or baby torso. A disposable diaper or incontinence product was wrapped around the arm or baby torso, and loaded with saline. Hydration of the simulated skin was measured by evaporimetry and compared with clinical data from adult armband evaluations. RESULTS: The heated mechanical arm and baby torso accurately distinguished products for skin dryness. Eight diaper products were evaluated and compared to human test results. The torso in vitro and mechanical arm evaluations demonstrated strong correlations to human epidermal water loss evaluations, with repeatable results. Additionally, the bench test has been used for adult incontinence products, and it proved to differentiate those products as well as infant products. CONCLUSIONS: A rapid and reliable means of evaluation has been developed, and it is predictive of human subject testing.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".