Efficacy of water‐only or soap and water skin decontamination of chemical warfare agents or simulants using in vitro human models: A systematic review
Bibliographic record
Abstract
Water-only or water and soap are widely recommended as preferred solutions for dermal decontamination. However, limited efficacy data exist. We summarized experimental studies evaluating in vitro efficacy of water-only or soap and water in decontaminating chemical warfare agents (CWA) or their simulants from human skin models. Embase, Covidence®, MEDLINE, PubMed, Web of Science, and Google Scholar were searched for articles using water-only or soap and water decontamination methods for removal of CWA/CWA simulants in in vitro human skin models. Data extraction was completed from seven studies, yielding seven contaminants. Water-only decontamination led to partial decontamination in all skin samples (100%, n = 81/81). Soap and water decontamination led to partial decontamination in all skin samples (100%, n = 143/143). Four studies found decontamination to either paradoxically enhance absorption of contaminants or their penetration rates, known as the "wash-in" effect. Despite recommendations, water-only or water and soap decontamination were found to yield partial decontamination of CWA or their simulants in all human in vitro studies. Thus, more effective decontaminating agents are needed. Some studies demonstrated increased or faster penetration of chemicals following decontamination, which could prove deadly for agents such as VX, although these findings require in vivo validation. Heterogeneity in experimental setups limits interstudy comparison, and it remains unclear when water-only or water and soap are ideal decontaminants, which requires more studies. Pending manuscripts will summarize in vivo human and animal efficacy data. International harmonized efficacy protocol should enable more efficient public health decisions for evidence-based public health decisions.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.009 | 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".