Evidence map›Paper›PMID 35757837›Full record

Trial reportPrehospital and disaster medicine2022

Smart Glasses: A New Tool for Assessing the Number of Patients in Mass-Casualty Incidents.

Korakot Apiratwarakul, Lap Woon Cheung, Somsak Tiamkao, Pariwat Phungoen, Kitt Tientanopajai, Wiroj Taweepworadej, Wanida Kanarkard, Kamonwon Ienghong

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Prehospital and disaster medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 3 pooled it
–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

13 citing papers in PubMed, 3 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Observational
  5. Article
  6. Article
  7. Review
  8. Article
  9. Article
  10. Review
  11. Article
  12. Article
  13. 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

8 authors.

Korakot ApiratwarakulDepartment of Emergency Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.ORCID https://orcid.org/0000-0002-1984-0865
Lap Woon CheungAccident & Emergency Department, Princess Margaret Hospital, Kowloon, Hong Kong.
Somsak TiamkaoDepartment of Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Pariwat PhungoenDepartment of Emergency Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.
Kitt TientanopajaiDepartment of Computer Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand.
Wiroj TaweepworadejDepartment of Computer Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand.
Wanida KanarkardDepartment of Computer Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, Thailand.
Kamonwon IenghongDepartment of Emergency Medicine, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand.ORCID https://orcid.org/0000-0003-0328-4128

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionMass-casualty incidents (MCIs) are events in which many people are injured during the same period of time. This has major implications in regards to practical concerns and planning for both personnel and medical equipment. Smart glasses are modern tools that could help Emergency Medical Services (EMS) in the estimation of the number of potential patients in an MCI. However, currently there is no study regarding the advantage of employing the use of smart glasses in MCIs in Thailand. STUDY

objectiveThis study aims to compare the overall accuracy and amount of time used with smart glasses and comparing it to manual counting to assess the number of casualties from the scene.

methodsThis study was a randomized controlled trial, field exercise experimental study in the EMS unit of Srinagarind Hospital, Thailand. The participants were divided into two groups (those with smart glasses and those doing manual counting). On the days of the simulation (February 25 and 26, 2022), the participants in the smart glasses group received a 30-minute training session on the use of the smart glasses. After that, both groups of participants counted the number of casualties on the simulation field independently.

resultsSixty-eight participants were examined, and in the smart glasses group, a total of 58.8% (N = 20) of the participants were male. The mean age in this group was 39.4 years old. The most experienced in the EMS smart glasses group had worked in this position for four-to-six years (44.1%). The participants in the smart glasses group had the highest scores in accurately assessing the number of casualties being between 21-30 (98.0%) compared with the manual counting group (89.2%). Additionally, the time used for assessing the number of casualties in the smart glasses group was shorter than the manual counting group in tallying the number of casualties between 11-20 (6.3 versus 11.2 seconds; P = .04) and between 21-30 (22.1 versus 44.5 seconds; P = .02).

conclusionThe use of smart glasses to assess the number of casualties in MCIs when the number of patients is between 11 and 30 is useful in terms of greater accuracy and less time being spent than with manual counting.

Indexed as

Disaster PlanningEmergency Medical ServicesMass Casualty IncidentsSmart GlassesAdultComputer SimulationFemaleHospitalsHumansMaleTriageartificial intelligenceEmergency Medical Servicesmass-casualty incidentsprehospital emergency caresmart glasses

Identifiers

PMID35757837
PMCPMC9280067

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.