ArticlePloS one2023
Extended reality as a training method for medical first responders in mass casualty incidents: A protocol for a systematic review.
Article in PloS one, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed.
- Decision-making during training of a Swedish navy command and control team: a quantitative study of workload effects.Cognitive processing · 2025Article
- Innovative approaches to pericardiocentesis training: a comparative study of 3D-printed and virtual reality simulation models.Advances in simulation (London, England) · 2025Article
- Extended reality training for mass casualty incidents: a systematic review on effectiveness and experience of medical first responders.International journal of emergency medicine · 2024Review
- Operational Health Pavilions in Mass Disasters: Lessons Learned from the 2023 Earthquake in Turkey and Syria.Healthcare (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
INTRODUCTION/
backgroundMass-casualty incidents (MCIs) and disasters require an organised and effective response from medical first responders (MFRs). As such, novel training methods have emerged to prepare and adequately train MFRs for these challenging situations. Particular focus should be placed on extended reality (XR), which encompasses virtual, augmented and mixed reality (VR, AR, and MR, respectively), and allows participants to develop high-quality skills in realistic and immersive environments. Given the rapid evolution of high-fidelity simulation technology and its advantages, XR simulation has become a promising tool for emergency medicine. Accordingly, this systematic review aims to: 1) evaluate the effectiveness of XR training methods and 2) explore the experience of MFRs undergoing such training.
methodsA comprehensive search strategy will encompass four distinct themes: MFRs, disasters/MCIs, education and simulation, and XR. Four databases (MEDLINE, EMBASE, CINAHL and LILACs) will be searched along with an in-depth examination of the grey literature and reference lists of relevant articles. MetaQAT will be used as a study quality assessment tool and integrated into Covidence as part of the data extraction form. Given the predicted high heterogeneity between studies, it may not be possible to standardise data for quantitative comparison and meta-analysis. Thus, data will be synthesised in a narrative, semi-quantitative manner. DISCUSSION: This review will examine the existing literature on the effectiveness of XR simulation as a tool to train MFRs for MCIs, which could ultimately improve preparedness and response to disasters.
trial registrationProtocol registration: PROSPERO CRD42021275692.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.