Decontamination effectiveness and the necessity of innovation in a large-scale disaster simulation
Bibliographic record
Abstract
BACKGROUND: Chemical, biological, radiologic, nuclear, and explosive (CBRNE) events threaten the health and integrity of human populations across the globe. Effective decontamination is a central component of CBRNE disaster response. OBJECTIVE: This paper provides an objective determination of wet decontamination effectiveness through the use of a liquid-based contaminant proxy and describes the mobilization and adaptation of easily available materials for the needs of decontamination in pediatric victims. METHODS: In this in-situ disaster simulation conducted at a pediatric hospital, decontamination effectiveness was determined through a liquid-based contaminant proxy, and standard burn charts to systematically estimate affected total body surface area (TBSA) in 39 adult simulated patients. Two independent raters evaluated TBSA covered by the contaminant before and after decontamination. RESULTS: On average, simulated patients had 59 percent (95 percent CI [53, 65]) of their TBSA covered by the simulated contaminant prior to decontamination. Following a wet decontamination protocol, the average reduction in TBSA contamination was 81 percent (95 percent CI [74, 88]). There was high inter-rater reliability for TBSA assessment (intraclass correlation coefficient = 0.83, 95 percent CI [0.68, 0.92]. A modified infant bath was tested during the simulated decontamination of infant mannequins and thereafter integrated to the local protocol. CONCLUSION: Wet decontamination can remove more than 80 percent of the initial contaminant found on adult simulated patients. The use of a liquid-based visual tool as a contaminant proxy enables the inexpensive evaluation of decontamination performance in a simulated setting. This paper also describes an innovative, low-cost adaptation of a local decontamination protocol to better meet pediatric needs.
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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 | 0.022 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".