首页 /研究 /Discrimination, Reliability, Sensitivity, and Specificity of Robotic Surgical Proficiency Assessment With Global Evaluative Assessment of Robotic Skills and Binary Scoring Metrics: Results From a Randomized Controlled Trial
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Discrimination, Reliability, Sensitivity, and Specificity of Robotic Surgical Proficiency Assessment With Global Evaluative Assessment of Robotic Skills and Binary Scoring Metrics: Results From a Randomized Controlled Trial

Ruben De Groote, Stefano Puliatti, Marco Amato, Elio Mazzone, Alessandro Larcher, Rui Farinha, Artur de Oliveira Paludo, Liesbeth Desender, Nicolas Hubert, Ben Van Cleynenbreugel, Brendan Bunting, Alexandre Mottrie, Anthony G. Gallagher, Giuseppe Rosiello, Pieter Uvin, Jasper Decoene, Tom Tuyten, Mathieu D’Hondt, Charles Chatzopoulos, Bart De Troyer

发表年份
2023
引用次数
10

摘要

Objective: To compare binary metrics and Global Evaluative Assessment of Robotic Skills (GEARS) evaluations of training outcome assessments for reliability, sensitivity, and specificity. Background: GEARS-Likert-scale skills assessment are a widely accepted tool for robotic surgical training outcome evaluations. Proficiency-based progression (PBP) training is another methodology but uses binary performance metrics for evaluations. Methods: In a prospective, randomized, and blinded study, we compared conventional with PBP training for a robotic suturing, knot-tying anastomosis task. Thirty-six surgical residents from 16 Belgium residency programs were randomized. In the skills laboratory, the PBP group trained until they demonstrated a quantitatively defined proficiency benchmark. The conventional group were yoked to the same training time but without the proficiency requirement. The final trial was video recorded and assessed with binary metrics and GEARS by robotic surgeons blinded to individual, group, and residency program. Sensitivity and specificity of the two assessment methods were evaluated with area under the curve (AUC) and receiver operating characteristics (ROC) curves. Results: = 0.033). The mean interrater reliability for binary metrics and GEARS was 0.87 and 0.38, respectively. Binary total error metrics AUC was 97% and for GEARS 85%. With a sensitivity threshold of 0.8, false positives rates were 3% and 25% for, respectively, the binary and GEARS assessments. Conclusions: Binary metrics for scoring a robotic VUA task demonstrated better psychometric properties than the GEARS assessment.

关键词

Receiver operating characteristicInter-rater reliabilityRandomized controlled trialBinary classificationArtificial intelligenceReliability (semiconductor)Medical physicsMachine learningComputer scienceStatistics

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