Evidence map›Paper›PMID 30723539›Full record

ArticleJournal of healthcare engineering2019

Towards Virtual VATS, Face, and Construct Evaluation for Peg Transfer Training of Box, VR, AR, and MR Trainer.

Zhibao Qin, Yonghang Tai, Chengqi Xia, Jun Peng, Xiaoqiao Huang, Zaiqing Chen, Qiong Li, Junsheng Shi

Abstract readComparative StudyEvaluation StudyValidation Study
In one paragraph

Article in Journal of healthcare engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 2 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

10 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Extended, virtual and augmented reality in thoracic surgery: a systematic review.Interactive cardiovascular and thoracic surgery · 2022
    Pooled it
  3. Trial
  4. Review
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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.

Zhibao QinYunnan Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming 650000, China.
Yonghang TaiYunnan Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming 650000, China.ORCID 0000-0001-9186-475X
Chengqi XiaYunnan Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming 650000, China.
Jun PengDepartment of Thoracic Surgery, Yunnan First People's Hospital, Kunming 650000, China.
Xiaoqiao HuangYunnan Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming 650000, China.
Zaiqing ChenYunnan Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming 650000, China.
Qiong LiYunnan Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming 650000, China.
Junsheng ShiYunnan Key Laboratory of Opto-Electronic Information Technology, Yunnan Normal University, Kunming 650000, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study is to develop and assess the peg transfer training module face, content and construct validation use of the box, virtual reality (VR), cognitive virtual reality (CVR), augmented reality (AR), and mixed reality (MR) trainer, thereby to compare advantages and disadvantages of these simulators. Training system (VatsSim-XR) design includes customized haptic-enabled thoracoscopic instruments, virtual reality helmet set, endoscope kit with navigation, and the patient-specific corresponding training environment. A cohort of 32 trainees comprising 24 novices and 8 experts underwent the real and virtual simulators that were conducted in the department of thoracic surgery of Yunnan First People's Hospital. Both subjective and objective evaluations have been developed to explore the visual and haptic potential promotions in peg transfer education. Experiments and evaluation results conducted by both professional and novice thoracic surgeons show that the surgery skills from experts are better than novices overall, AR trainer is able to provide a more balanced training environments on visuohaptic fidelity and accuracy, box trainer and MR trainer demonstrated the best realism 3D perception and surgical immersive performance, respectively, and CVR trainer shows a better clinic effect that the traditional VR trainer. Combining these in a systematic approach, tuned with specific fidelity requirements, medical simulation systems would be able to provide a more immersive and effective training environment.

Indexed as

AdultAugmented RealityClinical CompetenceComputer-Assisted InstructionComputer SimulationFemaleHumansLung NeoplasmsMaleMiddle AgedSoftwareThoracic Surgery, Video-AssistedUser-Computer InterfaceVirtual RealityYoung Adult

Identifiers

PMID30723539
PMCPMC6339710

What OpenQuestion holds

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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.