Qi Di, Petrusa Emil, Kruger Uwe, Milef Nicholas, Abu-Nuwar Mohamad Rassoul, Haque Mohamad, Lim Robert, Jones Daniel B, Turkseven Melih, Demirel Doga, Halic Tansel, De Suvranu, Saillant Noelle
Center for Modeling, Simulation, and Imaging in Medicine, Rensselaer Polytechnic Institute, Troy, New York.
Department of Surgery, Massachusetts General Hospital, Harvard School of Medicine, Boston, Massachusetts.
J Surg Res. 2020 Aug;252:247-254. doi: 10.1016/j.jss.2020.03.021. Epub 2020 Apr 15.
Discriminating performance of learners with varying experience is essential to developing and validating a surgical simulator. For rare and emergent procedures such as cricothyrotomy (CCT), the criteria to establish such groups are unclear. This study is to investigate the impact of surgeons' actual CCT experience on their virtual reality simulator performance and to determine the minimum number of actual CCTs that significantly discriminates simulator scores. Our hypothesis is that surgeons who performed more actual CCT cases would perform better on a virtual reality CCT simulator.
47 clinicians were recruited to participate in this study at the 2018 annual conference of the Society of American Gastrointestinal and Endoscopic Surgeons. We established groups based on three different experience thresholds, that is, the minimal number of CCT cases performed (1, 5, and 10), and compared simulator performance between these groups.
Participants who had performed more clinical cases manifested higher mean scores in completing CCT simulation tasks, and those reporting at least 5 actual CCTs had significantly higher (P = 0.014) simulator scores than those who had performed fewer cases. Another interesting finding was that classifying participants based on experience level, that is, attendings, fellows, and residents, did not yield statistically significant differences in skills related to CCT.
The simulator was sensitive to prior experience at a threshold of 5 actual CCTs performed.
评估不同经验学习者的鉴别表现对于开发和验证手术模拟器至关重要。对于诸如环甲膜切开术（CCT）等罕见且紧急的手术，建立此类分组的标准尚不清楚。本研究旨在调查外科医生实际的CCT经验对其虚拟现实模拟器表现的影响，并确定能显著区分模拟器分数的实际CCT的最少例数。我们的假设是，实施更多实际CCT病例的外科医生在虚拟现实CCT模拟器上表现会更好。
在2018年美国胃肠内镜外科医师学会年会上招募了47名临床医生参与本研究。我们根据三个不同的经验阈值建立分组，即实施的CCT病例最少例数（1、5和10），并比较这些组之间的模拟器表现。
实施更多临床病例的参与者在完成CCT模拟任务时表现出更高的平均分数；报告至少实施5例实际CCT的参与者的模拟器分数显著高于实施病例较少者（P = 0.014）。另一个有趣的发现是，根据经验水平（即主治医生、研究员和住院医生）对参与者进行分类，在与CCT相关的技能方面未产生统计学上的显著差异。
该模拟器对实施5例实际CCT这一经验阈值敏感。
