A Comparison of Robotic Simulation Performance on Basic Virtual Reality Skills: Simulator Subjective Versus Objective Assessment Tools.

作者: Ariel K. Dubin , Roger Smith , Danielle Julian , Alyssa Tanaka , Patricia Mattingly

DOI: 10.1016/J.JMIG.2017.07.019

关键词: Randomized controlled trialSimulationRating scaleMotion (physics)Robotic surgeryPerformance metricMetric (unit)MedicineVirtual realityLikert scale

摘要: Abstract Study Objective To answer the question of whether there is a difference between robotic virtual reality simulator performance assessment and validated human reviewers. Current surgical education relies heavily on simulation. Several tools are available to trainee, including actual metrics Global Evaluative Assessment Robotic Skills (GEARS) metrics, both which have been independently validated. GEARS rating scale through evaluators can score trainees' performances 6 domains: depth perception, bimanual dexterity, efficiency, force sensitivity, autonomy, control. Each domain scored 5-point Likert with anchors. We used 2 common simulators, dV-Trainer (dVT; Mimic Technologies Inc., Seattle, WA) da Vinci Simulator (dVSS; Intuitive Surgical, Sunnyvale, CA), compare simulators for basic task each simulator. Design A prospective single-blinded randomized study. Setting training center. Participants Surgeons surgeons in training. Interventions Demographic information was collected sex, age, level training, specialty, previous experience. Subjects performed trials ring rail 1 (RR1) (dVSS dVT) after undergoing randomization warm-up exercises. The second RR1 trial recorded, deidentified videos were sent reviewers using GEARS. Eight different identified paired similar metric tool. evaluation scores Spearman rho calculated their correlation. Measurements Main Results Seventy-four subjects enrolled this study 9 excluded missing or incomplete data. There strong correlation time complete versus total score, economy motion overall coefficients greater than equal 0.70; these significant (p  Conclusion On VR tasks, several well matched assigned by reviewers, but others not. Identifying matches/mismatches improve process when simulators.

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