Evidence map›Paper›PMID 41654858›Full record

Trial reportBMC medical education2026

Effectiveness of VR and traditional training in medical education for mass casualty management: an OSCE-based randomized controlled trial.

Zhe Li, Wan Chen, Guozheng Qiu, Lei Shi, Yutao Tang, Xibin Xu, Sanshan Zhu, Liwen Lyu

Abstract readRandomized Controlled Trial
In one paragraph

Trial report in BMC medical education, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Zhe LiDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China.
Wan ChenDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China.
Guozheng QiuDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China.
Lei ShiDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China.
Yutao TangDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China.
Xibin XuDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China.
Sanshan ZhuDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China.
Liwen LyuDepartment of Emergency, The People's Hospital of Guangxi Zhuang Autonomous Region, Guangxi Academy of Medical Sciences, Nanning, Guangxi, 530021, China. iculvliwen@163.com.

Funding

Guangxi Medical and Health Appropriate Technology Development and Promotion Application Project S2023013Natural Science Foundation of Guangxi Zhuang Autonomous Region 2024GXNSFAA010071
6 · The paper itself

Abstract

objectiveThis study aimed to evaluate the effectiveness of VR-based training compared to traditional lecture-based training for medical trainees in managing MCIs, specifically focusing on road traffic accidents. The primary assessment was performed using an Objective Structured Clinical Examination (OSCE) and a theoretical knowledge test.

methodsA randomized controlled trial was conducted with 46 medical trainees receiving emergency medicine training. Participants were randomly assigned to either a VR-based Training Group or a Lecture-based Training Group, with each group receiving a 2-hour training session on mass casualty management. The training effectiveness was evaluated through pre- and post-training knowledge tests, OSCE performance, and post-training feedback questionnaires. Statistical analyses were performed to compare the two groups.

resultsBaseline characteristics were well-matched between groups. The VR-based Training Group demonstrated significantly higher post-test scores (83.96 ± 13.11) compared to the Lecture-based Training Group (72.17 ± 20.89, p = 0.03). The learning gain was also significantly greater in the VR-based Training Group (40.26 ± 15.61) compared to the Lecture-based Training Group (28.26 ± 17.04, p = 0.02). OSCE results showed that the VR-based Training Group demonstrated better performance than the Lecture-based Training Group across all stations, with significant improvements in triage, injury assessment, and overall scene management. Additionally, feedback from the post-training questionnaire revealed that the VR-based Training Group reported greater confidence in performing critical tasks.

conclusionVR-based training may represent a more effective and engaging approach for teaching mass casualty management relative to traditional lecture-based methods. It may enhance both theoretical knowledge and practical skills, which could help better prepare medical trainees for high-pressure scenarios such as MCIs. As VR technology continues to evolve, its integration into medical education holds considerable potential for improving preparedness and clinical performance in emergency settings.

Indexed as

Emergency MedicineMass Casualty IncidentsVirtual RealityAccidents, TrafficAdultClinical CompetenceEducational MeasurementFemaleHumansMaleMass casualty managementMedical educationOSCE stationRoad traffic accidentsVirtual reality

Identifiers

PMID41654858
PMCPMC12977882

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.