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开发一个分类系统,用于实时手术反.
Elyssa Y Wong1, Timothy N Chu1, Runzhuo Ma1
1Center for Robotic Simulation and Education, Catherine and Joseph Aresty Department of Urology, USC Institute of Urology, University of Southern California, Los Angeles.
JAMA network open
|June 28, 2023
概括
一个新的系统可靠地分类手术反,包括触发因素,反类型和实习生反应. 这种方法可以增强不同专业和经验水平的外科教育策略.
科学领域:
- 医学教育 医学教育
- 进行外科手术培训.
- 医疗信息学 医疗信息学
背景情况:
- 现场手术内反对于外科手术技能的发展至关重要.
- 目前还没有标准化的方法来描述外科手术反.
研究的目的:
- 在活体外科病例中量化手术内反.
- 为分析外科手术反提出一个标准化的解构.
主要方法:
- 用混合方法分析音频视频录制的手术的定性研究.
- 在机器人教学案例中涉及泌尿科医生,研究员和主持外科医生.
- 对转录的反数据进行代编码,以识别反复出现的主题.
主要成果:
- 触发因素,反和响应的分类系统显示了中度至实质性的interrater可靠性.
- 对6项外科手术的3711个反实例的分析显示,根据外科医生的经验和手术任务,有显著差异.
- 特定的反类型,如具有视觉组件的技术反,与学员行为变化或承认的增加有关.
结论:
- 开发了一种可行和可靠的方法来分类机器人手术过程中的外科反.
- 拟议的系统可以跨外科专业和实习生经验水平进行概括.
- 这种分类系统可以为新的外科教育策略提供信息.
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