Evidence map›Paper›PMID 40583862›Full record

ArticleMedEdPORTAL : the journal of teaching and learning resources2025

Low-Fidelity, In Situ, Accessible Pediatric Mass Casualty Incident Simulation to Evaluate and Improve Pediatric Readiness.

Sydney E Jeffs, Cathlyn K Medina, Parker Frankiewicz, Steven W Thornton, Elizabeth Horne, Smith Ngeve, Tara Thomason, Delaney Anani-Wolf, Catherine B Beckhorn, Delaney James and 7 more

Abstract read
In one paragraph

Article in MedEdPORTAL : the journal of teaching and learning resources, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

17 authors.

Sydney E JeffsMedical Student, Duke University School of Medicine.ORCID https://orcid.org/0000-0002-1761-8566
Cathlyn K MedinaMedical Student, Duke University School of Medicine.
Parker FrankiewiczMedical Student, Duke University School of Medicine.
Steven W ThorntonSurgery Resident, Department of Surgery, Duke University School of Medicine.
Elizabeth HorneSurgery Resident, Johns Hopkins Hospital.
Smith NgeveMedical Student, University of North Carolina at Chapel Hill School of Medicine.
Tara ThomasonMedical Student, Duke University School of Medicine.
Delaney Anani-WolfMedical Student, Duke University School of Medicine.
Catherine B BeckhornMedical Student, Duke University School of Medicine.
Delaney JamesNurse Manager, Department of Emergency Medicine, Duke University Hospital.
Rachel HobbsNurse Practitioner, Department of Emergency Medicine, Duke University Hospital.
Remi HueckelAssociate Professor of Nursing, Division of Pediatric Critical Care, Duke University School of Nursing.
Corrie E ChumpitaziProfessor of Pediatrics, Division of Pediatric Emergency Medicine, Department of Pediatrics, Duke University School of Medicine.
Erin R HanlinAssistant Professor of Emergency Medicine, Department of Emergency Medicine, Duke University School of Medicine.
Rachel O'BrianAssistant Professor of Pediatrics, Division of Pediatric Emergency Medicine, Department of Pediatrics, Duke University School of Medicine.
Elisabeth T TracyPediatric Surgeon, Division of Pediatric Surgery, Department of Surgery, Duke University School of Medicine.
Emily GreenwaldAssistant Professor of Pediatrics, Division of Pediatric Emergency Medicine, Department of Pediatrics, Duke University School of Medicine.

Funding

Medical Scientist Training Program Training GrantT32GM145449 · NIGMS · DUKE UNIVERSITY · PI Christopher D Kontos · 2022 to 2026
$6.6M
NIGMS NIH HHS T32 GM145449
6 · The paper itself

Abstract

Introduction: Existing mass casualty incident (MCI) simulations rely on high-fidelity patient simulators, which are cost-prohibitive and often exclude pediatric patients. To address the need for deployable, low-fidelity pediatric MCI simulations, we developed and evaluated a cost-conscious model to teach the principles of JumpSTART, the pediatric variation of the Simple Triage and Rapid Treatment (START) algorithm. Methods: In this low-fidelity pediatric MCI simulation, pediatric trauma patients were represented by 2D, life-sized drawings including all pertinent information for triage using JumpSTART. Learners were prehospital and hospital staff with multidisciplinary backgrounds. Learners were divided into two groups and assigned five unique patients across triage and acuity levels. Primary outcomes were the accuracy of assigned triage categories and Broselow lengths, and time to triage completion. Postsimulation surveys were designed to assess learner attitudes about the exercise. Results: Two sessions of the pediatric MCI simulation were conducted (18 and 16 participants, respectively). Triage categories were correctly assigned using JumpSTART for 9 of 10 patients in cohort 1. One patient was over-triaged. All patients in cohort 2 were correctly assigned triage categories. Broselow lengths were correctly assigned to all patients. Median time to assign a triage category per patient was 67 seconds (range 30-135) for the first cohort and 64 seconds (range 30-116) for the second. Participant feedback was universally positive. Discussion: We present an accessible, low-fidelity training model for pediatric MCI, which creates a simple but dynamic hands-on experience for participants around the JumpSTART pediatric triage algorithm and is replicable across environments.

Indexed as

Mass Casualty IncidentsPediatricsSimulation TrainingAlgorithmsChildFemaleHumansMalePatient SimulationSurveys and QuestionnairesTriageCase-Based LearningCommunication SkillsInterdisciplinary MedicineInterprofessional EducationMass Casualty IncidentPediatricsSimulationTriage

Identifiers

PMID40583862
PMCPMC12202713

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.