Efficacy of water skin decontamination in vivo in humans: A systematic review
Bibliographic record
Abstract
With the constant possibility of occupational exposures, chemical warfare, and targeted attacks, increased attention has been given to determining effective and timely dermal decontamination strategies. This systematic review summarises experimental studies reporting decontamination with water-based solutions of dermal chemical contaminants with in vivo human data. Embase, MEDLINE, PubMed, Web of Science, and Google Scholar databases were comprehensively searched using search terms ("cutaneous" or "skin" or "dermal" or "percutaneous") and ("decontamination" or "decontaminant" or "skin decontamination") to include 10 studies, representing 18 chemical contaminants, 199 participants, and 351 decontamination outcomes. Three studies included data from decontamination with water (10.8%, n = 38/351 decontamination outcomes), seven with soap and water (68.4%, n = 240/351 decontamination outcomes), and two with 10% isopropanol distilled water (20.8%, n = 73/351 decontamination outcomes). Results of dermal decontamination using water showed complete decontamination (CD) outcomes in 52.6% (n = 20/38) and partial decontamination (PD) in 47.4% (n = 18/38); using soap and water showed PD outcomes in 92.9% (n = 223/240) and minimal to no effect in 7.1% (n = 17/240); and using 10% isopropanol distilled water achieved PD outcomes in 100.0% (n = 73/73). Available data show that decontamination with water, soap and water, and 10% isopropanol distilled water is incomplete. Much remains to be learned about decontamination of the large variety of chemical contaminants including a range of molecular weights, lipid and water solubilities, melting points, volatility, and hydrogen bonds, as well as clinically relevant anatomic sites. A major void exists in data confirming or denying the completeness of decontamination by measuring absorption and excretion. The development of effective decontamination solutions is of high priority.
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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 | 0.008 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.008 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".