542 Mobile Burn Response Teams: A Scoping Review
Bibliographic record
Abstract
Abstract Introduction Advancements in the treatment of burns have reduced morbidity. However, the resources needed to deliver up-to-date 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 including burn patients. The aim of this scoping review was to identify existing literature regarding burn management 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 evolved. Methods We conducted a scoping review of all literature types published up to February 2021. 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. Results Of 5,737 identified reports, 24 publications were reviewed. Three distinct 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 US Army Burn Flight Team. Teams have responded to events such as terrorist attacks by providing specialized burn supplies and personnel. Mobile burn response teams have demonstrated expert triage and stabilization advantages but are limited by the number of deployable specialists. A challenge in deploying a mobile team is the removal of experts from a burn centre. Conclusions 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 centre experts to manage triage may address these challenges.
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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 | 0.010 | 0.057 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.022 | 0.022 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.001 |
| 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; 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".