Synthetic Simulators for Microsurgery Training: A Systematic Review

Author:

Sapino Gianluca1,Gonvers Stephanie1,Cherubino Mario2,Ballestín Alberto3,di Summa Pietro Giovanni1

Affiliation:

1. Department of Plastic, Reconstructive, Aesthetic and Hand Surgery, University Hospital of Lausanne (CHUV), University of Lausanne, Lausanne, Switzerland

2. Department of Plastic and Hand Surgery, University Hospital of Varese, University of Varese, Varese, Italy

3. Tumor Microenvironment Laboratory, UMR3347 CNRS/U1021 INSERM, Institut Curie, Orsay—Paris, France.

Abstract

Background: Microsurgery has a steep learning curve. Synthetic simulators have proven to be useful training tools for the initial learning stages, as well as being ethically sound, viable, safe, and cost-effective. The objective of this review was to determine the quality, effectiveness, and validity of these simulators as well as to assess their ability to evaluate microsurgical skills. Methods: A systematic review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was performed. We searched databases (Web of Science, Scopus, and PubMed) to identify original articles describing synthetic training models for microsurgery. Three reviewers evaluated articles for inclusion following predefined selection criteria. Data were extracted from full-texts of included articles. Results: Thirty-nine studies met the inclusion criteria. A total of 38 different devices have been recorded. Microsurgical training devices offer a low-cost, fast, and consistent method to concretely quantify and assess the initial microsurgical skills of trainees using standardized exercises that can be scored by the examiner. According to the authors, the outcomes were satisfactory, with a tangible improvement in microsurgical abilities, despite the lack of a common comparison scale. Conclusions: Thanks to their availability, cost, and effectiveness, synthetic models are the recommended option to train basic, intermediate and advanced procedures before executing them on in vivo models.

Publisher

Ovid Technologies (Wolters Kluwer Health)

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3