Evidence map›Paper›PMID 37342081›Full record

ArticleJournal of medical Internet research2023

Novel Software for High-level Virological Testing: Self-Designed Immersive Virtual Reality Training Approach.

Huey-Pin Tsai, Che-Wei Lin, Ying-Jun Lin, Chun-Sheng Yeh, Yan-Shen Shan

Abstract read
In one paragraph

Article in Journal of medical Internet research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 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

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Trial
  3. Review
  4. Article
  5. Review
  6. Review
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

5 authors.

Huey-Pin Tsai *Department of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0003-1248-4877
Che-Wei Lin *Medical Device Innovation Center, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0002-1894-1189
Ying-Jun LinDepartment of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0002-4906-0974
Chun-Sheng YehDepartment of Pathology, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0001-9567-2552
Yan-Shen ShanInstitute of Clinical Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan.ORCID 0000-0002-2457-5189

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundTo ensure the timely diagnosis of emerging infectious diseases, high-tech molecular biotechnology is often used to detect pathogens and has gradually become the gold standard for virological testing. However, beginners and students are often unable to practice their skills due to the higher costs associated with high-level virological testing, the increasing complexity of the equipment, and the limited number of specimens from patients. Therefore, a new training program is necessary to increase training and reduce the risk of test failure.

objectiveThe aim of the study is to (1) develop and implement a virtual reality (VR) software for simulated and interactive high-level virological testing that can be applied in clinical practice and skills building or training settings and (2) evaluate the VR simulation's effectiveness on reaction, learning, and behavior of the students (trainees).

methodsViral nucleic acid tests on a BD MAX instrument were selected for our VR project because it is a high-tech automatic detection system. There was cooperation between teachers of medical technology and biomedical engineering. Medical technology teachers were responsible for designing the lesson plan, and the biomedical engineering personnel developed the VR software. We designed a novel VR teaching software to simulate cognitive learning via various procedure scenarios and interactive models. The VR software contains 2D VR "cognitive test and learning" lessons and 3D VR "practical skills training" lessons. We evaluated students' learning effectiveness pre- and posttraining and then recorded their behavior patterns when answering questions, performing repeated exercises, and engaging in clinical practice.

resultsThe results showed that the use of the VR software met participants' needs and enhanced their interest in learning. The average posttraining scores of participants exposed to 2D and 3D VR training were significantly higher than participants who were exposed solely to traditional demonstration teaching (P<.001). Behavioral assessments of students pre- and posttraining showed that students exposed to VR-based training to acquire relevant knowledge of advanced virological testing exhibited significantly improved knowledge of specific items posttraining (P<.01). A higher participant score led to fewer attempts when responding to each item in a matching task. Thus, VR can enhance students' understanding of difficult topics.

conclusionsThe VR program designed for this study can reduce the costs associated with virological testing training, thus, increasing their accessibility for students and beginners. It can also reduce the risk of viral infections particularly during disease outbreaks (eg, the COVID-19 pandemic) and also enhance students' learning motivation to strengthen their practical skills.

Indexed as

COVID-19Virtual RealityHumansLearningPandemicsSoftwarebiotechnologyclinical practicedesigndetectiondevelopmenthigh-level clinical virologyimmersivemedical educationmolecularpathogensimulationskill trainingsoftwareteachingtestingvirologicalvirologyvirtual realityVR

Identifiers

PMID37342081
PMCPMC10337313

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.