External validation of Global Evaluative Assessment of Robotic Skills (GEARS)

Aghazadeh, Monty; Jayaratna, Isuru; Hung, Andrew J.; Pan, Michael; Desai, Mihir; Gill, Inderbir S.; Goh, Alvin C.

doi:10.1007/s00464-015-4070-8

Cited by 103 publications

(67 citation statements)

References 13 publications

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“…Curricula are being developed for use with the robotic surgical simulators that aim to train utilizers to proficiency standards [40,41]. In parallel, global evaluative assessment tools to specifically rate robotic surgical skill are being developed [42]. As these systems become more developed and utilized, perhaps the robotic revolution will follow the widespread adaptation of laparoscopy and training will take place during residency and fellowship training, obviating the need for mid-or late-career skill acquisition.…”

Section: Discussionmentioning

confidence: 99%

An appraisal of the learning curve in robotic general surgery

et al. 2017

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Although robotic surgery is increasingly utilized in general surgery, the literature provides few guidelines on the learning curve for adoption. In this heterogeneous sample of reviewed articles, the number of cases needed to achieve plateau performance varies by case type and the learning curve may have multiple phases as surgeons add more complex cases to their case mix with growing experience. Time is the most common determinant for the learning curve. The literature lacks a uniform assessment of outcomes and complications, which would arguably reflect expertise in a more meaningful way than time to perform the operation alone.

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Section: Discussionmentioning

confidence: 99%

An appraisal of the learning curve in robotic general surgery

et al. 2017

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“…To highlight the range of participant experience levels, demographic data were stratified into expert and trainee groups and the data compared across groups using the Mann-Whitney U-test for categorical data and the chi-squared/Fisher's exact test for nominal data. To remain consistent with previous studies, experts were defined as participants with >30 robotic cases as primary surgeon [10,12,13]. Experience level stratification was not statistically necessary for the primary outcomes of the study, but was carried out to allow contemporary comparison.…”

Section: Discussionmentioning

confidence: 99%

“…Despite this rapid surge in use, the development and validation of training methods has failed to keep pace. Several training platforms have undergone validation testing, including inanimate tasks [1][2][3][4][5], virtual reality exercises [6][7][8][9] and ex vivo models [5,9,10]. Validation work in these studies includes demonstration of face (realism of training tool), content (usefulness as training tool), construct (ability to distinguish between different skill levels) and cross-method (correlation across training methods) validity.…”

Section: Introductionmentioning

confidence: 99%

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Performance of robotic simulated skills tasks is positively associated with clinical robotic surgical performance

et al. 2016

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ObjectiveTo compare user performance of four fundamental inanimate robotic skills tasks (FIRST) as well as eight da Vinci Skills Simulator (dVSS) virtual reality tasks with intra-operative performance (concurrent validity) during robot-assisted radical prostatectomy (RARP) and to show that a positive correlation exists between simulation and intra-operative performance. Materials and MethodsA total of 21 urological surgeons with varying robotic experience were enrolled. Demographics were captured using a standardized questionnaire. User performance was assessed concurrently in simulated (FIRST exercises and dVSS tasks) and clinical environments (endopelvic dissection during RARP). Intra-operative robotic clinical performance was scored using the previously validated sixmetric Global Evaluative Assessment of Robotic Skills (GEARS) tool. The relationship between simulator and clinical performance was evaluated using Spearman's rank correlation. ResultsPerformance was assessed in 17 trainees and four expert robotic surgeons with a median (range) number of previous robotic cases (as primary surgeon) of 0 (0-55) and 117 (58-600), respectively (P = 0.001). Collectively, the overall FIRST (q = 0.833, P < 0.001) and dVSS (q = 0.805, P < 0.001) simulation scores correlated highly with GEARS performance score. Each individual FIRST and dVSS task score also demonstrated a significant correlation with intra-operative performance, with the exception of Energy Switcher 1 exercise (P = 0.063). ConclusionsThis is the first study to show a significant relationship between simulated robotic performance and robotic clinical performance. Findings support implementation of these robotic training tools in a standardized robotic training curriculum.

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“…These cutoffs were selected based on external GEARS validation data demonstrating performance differences at this level. 11 Face validity was evaluated based on responses of all participants to questions 1 and 2 of the exit questionnaire. Content validity was derived from only experts' responses to questions 3-5.…”

Section: Discussionmentioning

confidence: 99%