Pediatric Decontamination Considerations in CBRN Events: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Children are uniquely vulnerable to chemical, biological, radiological, and nuclear (CBRN) events due to anatomical, physiological, and psychological differences. Current decontamination practices are adapted from adult protocols. OBJECTIVE: To evaluate current practices, challenges, and special considerations in pediatric decontamination during CBRN events. METHOD: A scoping review was conducted using six databases in accordance with PRISMA-ScR framework. Studies were eligible if they evaluated decontamination methods involving children (0-18 years) in real or simulated CBRN scenarios. Fourteen studies met the inclusion criteria, and data were thematically analyzed into four domains. RESULTS: Disrobing is widely recognized as a critical first step in the decontamination process, and 43% of the studies reviewed identified it as such. When done immediately and appropriately, it can remove a significant amount of contaminants. Although its effectiveness varies based on how much of the body is covered and the nature of the exposure. Dry decontamination was discussed in 21% of studies, and wet decontamination was the most commonly reported approach, appearing in 93%. Key pediatric challenges included hypothermia, psychological distress, separation from caregivers, and difficulties managing non-ambulatory or special needs populations. Few studies addressed age-specific protocols or long-term psychological impacts. The results are presented in procedural order to reflect the typical sequence of decontamination in CBRN response. CONCLUSIONS: Current decontamination guidelines inadequately address pediatric-specific needs. There is a critical need for standardized, age-appropriate guidelines that integrate caregiver support and psychosocial considerations. A pediatric decontamination algorithm was developed to consolidate current evidence into a practical framework for CBRN mass casualty incidents.
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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 | 0.019 | 0.080 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.014 | 0.016 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".