Mobile Burn Disaster Response Teams: A Scoping Review
Bibliographic record
Abstract
The resources needed to deliver modern burn care may be overwhelmed by mass casualty disasters. In 2021, the World Health Organization (WHO) recommended that countries prepare teams of deployable burn experts to assist with responding to a mass casualty disaster. The aim of this scoping review was to identify existing literature regarding burn mobile response team organization, describe the reported effectiveness of these teams, identify challenges in adopting the WHO recommendations, and consider how the recommendations may be reconsidered. We conducted a scoping review of all literature types published up to January 2022. Searches of MEDLINE, EMBASE, Scopus, and CINAHL databases were conducted to identify reports informing or reporting the use of mobile burn care specialty teams that respond to events resulting in multiple burn-injured victims, including pediatric victims and military response to civilian events. Of 6132 identified reports, 26 publications were reviewed. Three types of mobile burn response teams were identified: (1) teams organized by burn care networks, (2) government-organized medical disaster teams with burn-specific experts, and (3) the U.S. Army Burn Flight Team. Teams have responded to events such as terrorist attacks by providing specialized burn supplies and personnel. These teams have demonstrated expert triage and stabilization advantages but are limited by the number of deployable specialists. Although the WHO recommends increasing the number of mobile burn response teams available around the world, few countries have implemented this recommendation. A hybrid model where responders on scene communicate with burn center experts to manage triage may address these challenges.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.028 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 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; both teacher heads agree on what is shown here.
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".