Efficacy of soap and water‐based skin decontamination using in vitro animal models: A systematic review
Bibliographic record
Abstract
Water and/or soap and water solutions have historically been used as first-line decontamination strategies for a wide variety of dermal contaminants from workplace exposure, environmental pesticides, and civilian chemical warfare. Although water and/or soap and water solutions are often considered a gold standard of decontamination, many studies have found other decontamination methods to be superior. This systematic review summarizes the available data on in vitro animal models contaminated with a various chemicals and their decontamination with water and/or soap and water solutions using in vitro animal models. A comprehensive literature search was performed using Concordance, Embase, PubMed, Medline, Web of Science, and Google Scholar to find in vitro animal studies that provided data on dermal decontamination using water and/or soap and water solutions. Five studies were included that analyzed 11 contaminants across two in vitro animal models (rats and pigs). Water alone was used as a decontamination method for 63.6% of the contaminants (n = 7/11) and water and soap solutions for decontamination in 54.6% of contaminants (n = 6/11). Water alone provided incomplete contaminant removal of five of seven contaminants studied; soap and water did not show significant difference in decontamination when compared with other solutions for all four contaminants and was superior to water for both contaminants studied. Water and/or soap and water are used as decontamination strategies for a variety of dermal contamination events, but for many contaminants, they do not provide complete contamination when compared with newer decontamination solutions studied with in vitro animal models.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".