基于虚拟现实手术模拟器指标预测 GEARS 评分的模型。
A model for predicting the GEARS score from virtual reality surgical simulator metrics.
机构信息
Department of Obstetrics and Gynecology, Columbia University Medical Center, 622 W 168th Street PH 16-127, New York, NY, 10032, USA.
Florida Hospital Nicholson Center, Orlando, FL, USA.
出版信息
Surg Endosc. 2018 Aug;32(8):3576-3581. doi: 10.1007/s00464-018-6082-7. Epub 2018 Feb 5.
BACKGROUND
Surgical education relies heavily upon simulation. Assessment tools include robotic simulator assessments and Global Evaluative Assessment of Robotic Skills (GEARS) metrics, which have been validated. Training programs use GEARS for proficiency testing; however, it requires a trained human evaluator. Due to limited time, learners are reliant on surgical simulator feedback to improve their skills. GEARS and simulator scores have been shown to be correlated but in what capacity is unknown. Our goal is to develop a model for predicting GEARS score using simulator metrics.
METHODS
Linear and multivariate logistic regressions were used on previously reported data by this group. Subjects performed simple (Ring and Rail 1) and complex (Suture Sponge 1) tasks on simulators, the dV-Trainer (dVT) and the da Vinci Skills Simulator (dVSS). They were scored via simulator metrics and GEARS.
RESULTS
A linear model for each simulator and exercise showed a positive linear correlation. Equations were developed for predicting GEARS Total Score from simulator Overall Score. Next, the effects of each individual simulator metric on the GEARS Total Score for each simulator and exercise were examined. On the dVSS, Excessive Instrument Force was significant for Ring and Rail 1 and Instrument Collision was significant for Suture Sponge 1. On the dVT, Time to Complete was significant for both exercises. Once the significant variables were identified, multivariate models were generated. Comparing the predicted GEARS Total Score from the linear model (using only simulator Overall Score) to that using the multivariate model (using the significant variables for each simulator and exercise), the results were similar.
CONCLUSIONS
Our results suggest that trainees can use simulator Overall Score to predict GEARS Total Score using our linear regression equations. This can improve the training process for those preparing for high-stakes assessments.
背景
外科教育严重依赖模拟。评估工具包括机器人模拟器评估和全球机器人技能评估（GEARS）指标，这些都已经过验证。培训计划使用 GEARS 进行熟练程度测试；但是，它需要经过培训的人类评估者。由于时间有限，学习者依赖于手术模拟器的反馈来提高技能。GEARS 和模拟器分数已经显示出相关性，但具体情况尚不清楚。我们的目标是开发一种使用模拟器指标预测 GEARS 分数的模型。
方法
本研究组以前曾使用线性和多元逻辑回归对报告的数据进行分析。研究对象在模拟器、dV-Trainer（dVT）和达芬奇技能模拟器（dVSS）上执行简单（Ring and Rail 1）和复杂（Suture Sponge 1）任务。通过模拟器指标和 GEARS 对他们进行评分。
结果
每个模拟器和练习的线性模型均显示出正线性相关性。针对每个模拟器和练习，开发了从模拟器总评分预测 GEARS 总评分的方程。然后，检查了每个模拟器指标对每个模拟器和练习的 GEARS 总评分的影响。在 dVSS 上，仪器用力过大对 Ring and Rail 1 有显著影响，仪器碰撞对 Suture Sponge 1 有显著影响。在 dVT 上，完成时间对两项练习均有显著影响。一旦确定了显著变量，就会生成多元模型。将线性模型（仅使用模拟器总评分）预测的 GEARS 总评分与使用多元模型（使用每个模拟器和练习的显著变量）预测的 GEARS 总评分进行比较，结果相似。
结论
我们的结果表明，学员可以使用模拟器总评分通过我们的线性回归方程预测 GEARS 总评分。这可以改善那些准备接受高风险评估的学员的培训过程。
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