Evidence map›Paper›PMID 37891526›Full record

ArticleBMC medical education2023

Educational value of mixed reality combined with a three-dimensional printed model of aortic disease for vascular surgery in the standardized residency training of surgical residents in China: a case control study.

Weihao Li, Yuanfeng Liu, Yonghui Wang, Xuemin Zhang, Kun Liu, Yang Jiao, Xiaoming Zhang, Jie Chen, Tao Zhang

Abstract read
In one paragraph

Article in BMC medical education, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Integration of high-resolution mixed reality models into resident training.Journal of vascular surgery cases and innovative techniques · 2026
    Article
  2. Article
  3. 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

9 authors.

Weihao Li *Department of Vascular Surgery, Peking University People's Hospital, Beijing, 100044, PR China.
Yuanfeng Liu *Department of Vascular and Endovascular Surgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, PR China.
Yonghui Wang *Department of Critical Care Medicine, Peking University People's Hospital, Beijing, 100044, PR China.
Xuemin ZhangDepartment of Vascular Surgery, Peking University People's Hospital, Beijing, 100044, PR China.
Kun LiuDepartment of Vascular Surgery, Peking University People's Hospital, Beijing, 100044, PR China.
Yang JiaoBeijing Renxin Medical Technology Co., Ltd, Beijing, 100041, PR China.
Xiaoming ZhangDepartment of Vascular Surgery, Peking University People's Hospital, Beijing, 100044, PR China.
Jie ChenDepartment of Medical Quality Management, Peking University People's Hospital, Beijing, 100044, China. 53619253@qq.com.
Tao ZhangDepartment of Vascular Surgery, Peking University People's Hospital, Beijing, 100044, PR China. taozhang@bjmu.edu.cn.

Funding

Beijing Natural Science Foundation 7202214Beijing Natural Science Foundation 7224347National Natural Science Foundation of China 81970409Peking University Health Science Center Medical Education Research Funding Project 2020YB02
6 · The paper itself

Abstract

backgroundThe simulated three-dimensional (3D) printed anatomical model of the aorta, which has become the norm in medical education, has poor authenticity, tactility, feasibility, and interactivity. Therefore, this study explored the educational value and effect of mixed reality (MR) combined with a 3D printed model of aortic disease in training surgical residents.

methodFifty-one resident physicians who rotated in vascular surgery were selected and divided into traditional (27) and experimental (24) teaching groups using the random number table method. After undergoing the experimental and traditional training routines on aortic disease, both the groups took a theoretical test on aortic disease and an assessment of the simulation based on the Michigan Standard Simulation Experience Scale (MiSSES) template. Their scores and assessment results were compared. The study was conducted at the Department of Vascular Surgery of Peking University People's Hospital, Beijing, China.

resultsIn the theoretical test on aortic disease, the experimental teaching group obtained higher mean total scores (79.0 ± 9.1 vs. 72.6 ± 7.5, P = 0.013) and higher scores in anatomy/ pathophysiology (30.8 ± 5.4 vs. 24.8 ± 5.8; P < 0.001) than the traditional teaching group. The differences in their scores in the differential diagnosis (25.8 ± 3.0 vs. 23.3 ± 4.9; P = 0.078) and treatment (22.5 ± 11.8 vs. 24.5 ± 8.2; P = 0.603) sessions were insignificant. The MR-assisted teaching stratified the vascular residents through the MiSSES survey. Overall, 95.8% residents (23/24) strongly or somewhat agreed that the MR was adequately realistic and the curriculum helped improve the ability to understanding aortic diseases. Further, 91.7% residents (22/24) strongly or somewhat agreed that the MR-assisted teaching was a good training tool for knowledge on aortic diseases. All residents responded with "Good" or "Outstanding" on the overall rating of the MR experience.

conclusionsMR combined with the 3D printed model helped residents understand and master aortic disease, particularly regarding anatomy and pathophysiology. Additionally, the realistic 3D printing and MR models improved the self-efficacy of residents in studying aortic diseases, thus greatly stimulating their enthusiasm and initiative to study.

Indexed as

Aortic DiseasesAugmented RealityInternship and ResidencyCase-Control StudiesClinical CompetenceHumansPrinting, Three-DimensionalVascular Surgical ProceduresMiSSESMixed realityStandardized residency trainingThree-dimensional printed modelVascular surgery

Identifiers

PMID37891526
PMCPMC10612237

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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.