Optimizing Skin Protection with Semipermeable Gloves
Bibliographic record
Abstract
BACKGROUND: Occlusion due to gloves is one important cause of glove irritation. Macerated softened skin gives poor protection against microbes and chemical injuries. The introduction of a breathable protective glove material would represent a significant step toward improved prevention of occupational skin disease. OBJECTIVE: Performance levels of semipermeable and occlusive gloves were examined under conditions typical of the hairdressing profession. In two studies, tests comparing breathable semipermeable gloves to single-use gloves made of occlusive materials were conducted. METHODS: In an initial study, a user survey was carried out in conjunction with bioengineering examinations. Values at baseline and values after gloves were worn were recorded by measuring transepidermal water loss (TEWL), skin humidity (SH), and skin surface hydrogen ion concentration (pH) in 20 healthy volunteers. In a second study, the gloves were tested for penetrability and permeability with three chemical compounds typically used in the hairdressing profession. RESULTS: Bioengineering examination objectively confirmed users' reports of reduced hand perspiration when semipermeable gloves were worn. The TEWL, SH, and skin surface pH values remained largely stable after 20 minutes of wearing semipermeable gloves, in contrast to the reactions observed with gloves of occlusive materials. Permeability tests indicated that the semipermeable material is effective, with some restrictions. Air leakage testing revealed that all 50 gloves tested were not airtight. CONCLUSION: Following the optimization of manufacturing methods, additional tests of the penetrability of semipermeable gloves will be necessary.
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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 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".