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MP20-18 CONCURRENT VALIDITY OF A SIMULATED INANIMATE MODEL FOR PHYSICAL LEARNING EXPERIENCE IN PARTIAL NEPHRECTOMY (SIMPLE-PN)

Braden Candela, Jonathan Stone, Jennifer Park, Guandong Wu, Hani Rashid, Jean Joseph, Ahmed Ghazi

Year
2016
Citations
5
Access
Open access

Abstract

You have accessJournal of UrologySurgical Technology & Simulation: Training & Skills Assessment II1 Apr 2016MP20-18 CONCURRENT VALIDITY OF A SIMULATED INANIMATE MODEL FOR PHYSICAL LEARNING EXPERIENCE IN PARTIAL NEPHRECTOMY (SIMPLE-PN) Braden Candela, Jonathan Stone, Jennifer Park, Guan Wu, Hani Rashid, Jean Joseph, and Ahmed Ghazi Braden CandelaBraden Candela More articles by this author , Jonathan StoneJonathan Stone More articles by this author , Jennifer ParkJennifer Park More articles by this author , Guan WuGuan Wu More articles by this author , Hani RashidHani Rashid More articles by this author , Jean JosephJean Joseph More articles by this author , and Ahmed GhaziAhmed Ghazi More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2016.02.2787AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The implementation of surgical simulators as training & assessment tools requires extensive validation. We previously demonstrated face (realism), content (usefulness for training) & construct validity (differentiating novice & expert performance) of our simulated inanimate model for robot assisted partial nephrectomy (RAPN). In line with most robotic surgery simulators, concurrent validity (the extent to which the results generated from the simulator correlate with the gold standard) & predictive validity (extent to which an assessment will predict future performance) remain sparse. This is mainly due to complicated conversion algorithms required to compare objective, motion-based assessment metrics generated from simulators to subjective assessment of live surgical performance. From design to conception, we focused on the ability of our models to reproduce procedure specific performance metrics. We present the concurrent validity of SIMPLE-PN through a direct comparison of objective operative metrics from both simulated & live RAPN performance. METHODS A total of 8 robotic urologists (4 experts & 4 novice with > & < 250 upper tract robotic cases) performed all steps of a RAPN on the previously validated inanimate model. Live cases performed by the same urologists, with analogous nephrometry scores were identified over the preceding 6 months. Procedure-specific operative metrics (warm ischemia time-WIT, positive margins, urinary leak & estimated blood loss-EBL) recorded from both simulated & live cases were compared using paired t-tests. RESULTS No significant differences were found between the simulation & real cases for WIT (p=0.09, EBL (p=0.2), positive margins (p=0.2) or urinary leak (p=0.35). The lack of difference for operative performance still held true when comparing experts & novice groups separately. The largest difference was found for WIT followed by EBL & trended towards SIMPLE-PN being more difficult (table 1). CONCLUSIONS This study shows no difference between operative performance on real & simulated surgeries, thereby establishing the concurrent validity of SIMPLE-PN. This is the first report utilizing procedure-specific operative metrics for assessment of live surgery to objectively demonstrate concurrent validity of a training model © 2016FiguresReferencesRelatedDetails Volume 195Issue 4SApril 2016Page: e220 Advertisement Copyright & Permissions© 2016MetricsAuthor Information Braden Candela More articles by this author Jonathan Stone More articles by this author Jennifer Park More articles by this author Guan Wu More articles by this author Hani Rashid More articles by this author Jean Joseph More articles by this author Ahmed Ghazi More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

Keywords

Concurrent validityFace validityConstruct validityNephrectomyCriterion validityMedicinePredictive validityContent validityArtificial intelligenceComputer science

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