<i>In vitro</i> prediction of <i>in vivo</i> skin damage associated with the wiping of dry tissue against skin
Bibliographic record
Abstract
BACKGROUND: The ideal gentle cleansing product is one that effectively removes soils while minimizing damage to the skin. Thus, measuring physical abrasion caused by cleansing tissues is critical to the continued development of gentle cleansing products. Current analysis of cleansing materials for skin gentleness is time consuming and requires expensive human subject testing. This report describes the development of a rapid and inexpensive bench assay for the assessment of skin abrasion caused by wiping. METHODS: Coefficient of friction (COF) evaluations using bench methods were compared with results from clinical studies of repeated wiping and with confocal visualizations of excised skin. A Monitor/Slip and Friction instrument (model 32-06; TMI, Amityville, NY, USA) was used to measure tissue friction on simulated skin (Vitro-Skin, N19-5X; IMS, Milford, CT, USA). Clinical data from a 4-day repetitive forearm wiping study measuring transepidermal water loss (TEWL) in 30 subjects was compared with results from the bench top assay. In addition, excised skin samples were also treated using the COF bench assay and examined using confocal microscopy to visualize stratum corneum damage caused by wiping. RESULTS: Using the bench COF assay, we were able to distinguish between bath tissue codes by comparing average static friction value (ASFV) for the test codes, where lower ASFV indicated less abrasive tissue. The ASFV followed the same gentleness trend observed in the clinical study. Confocal microscopy of excised skin wiped with the same materials indicated stratum corneum damage consistent with the bench COF and clinical TEWL observations. CONCLUSION: We observed significant correlation between bench and clinical methods for measuring skin damage caused by wiping of skin with tissue. The bench method will facilitate rapid and inexpensive skin gentleness assessment of cleansing materials.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".