1216 FACE AND CONSTRUCT VALIDITY ASSESSMENT OF 2ND GENERATION ROBOTIC SURGERY SIMULATOR

2011 ◽  
Vol 185 (4S) ◽  
Author(s):  
Ruslan Korets ◽  
Joseph A. Graversen ◽  
Adam C. Mues ◽  
Mantu Gupta ◽  
Jaime Landman ◽  
...  
2018 ◽  
Vol 27 (1) ◽  
pp. 23-29 ◽  
Author(s):  
George Whittaker ◽  
Abdullatif Aydin ◽  
Sinthuri Raveendran ◽  
Faizan Dar ◽  
Prokar Dasgupta ◽  
...  

Background Training for robot-assisted thoracic lobectomy remains an issue, prompting the development of virtual reality simulators. Our aim was to assess the construct and face validity of a new thoracic lobectomy module on the RobotiX Mentor, a robotic surgery simulator. We also aimed to determine the acceptability and feasibility of implementation into training. Methods This prospective, observational, and comparative study recruited novice (n = 16), intermediate (n = 9), and expert (n = 5) participants from King's College London, the 25th European Conference on General Thoracic Surgery, and the Society of Robotic Surgery conference 2018. Each participant completed two familiarization tasks followed by the Guided Robotic Lobectomy module and an evaluation questionnaire. Outcome measures were compared using Mann-Whitney U tests. Results Construct validity was demonstrated in 12/21 performance evaluation metrics. Significant differences between groups were found in all metrics including: time taken to complete module, vascular injury, respect for tissue, number of stapler firings, time instruments out of view, number of instrument collisions, and number of movements. Participants deemed aspects of the simulator (mean 3/5) and module (3/5) as realistic and rated the simulator as both acceptable (3.8/5) and feasible (3.8/5) for robotic surgical training. Conclusions Face validity, acceptability, and feasibility were established for the thoracic lobectomy module of the RobotiX Mentor simulator. Moderate evidence of construct validity was also demonstrated. With further work, this simulation module could help to reduce the initial part of the learning curve for trainees and decrease the risk of errors during live training.


2011 ◽  
Vol 186 (3) ◽  
pp. 1019-1025 ◽  
Author(s):  
Andrew J. Hung ◽  
Pascal Zehnder ◽  
Mukul B. Patil ◽  
Jie Cai ◽  
Casey K. Ng ◽  
...  

2009 ◽  
Vol 181 (4S) ◽  
pp. 823-823 ◽  
Author(s):  
Thenkurussi Kesavadas ◽  
Amrish Kumar ◽  
Govindarajan Srimathveeravalli ◽  
Sridhar Karimpuzha ◽  
Rameela Chandrasekhar ◽  
...  

2011 ◽  
Vol 185 (4S) ◽  
Author(s):  
Andrea Gavazzi ◽  
Ali Bahsoun ◽  
Wim Van Haute ◽  
Kamran Ahmed ◽  
Mohammed Shamim Khan ◽  
...  

2013 ◽  
Vol 19 (4) ◽  
pp. 214-218 ◽  
Author(s):  
Brent A. Suozzi ◽  
David M. O’Sullivan ◽  
Kyle T. Finnegan ◽  
Adam C. Steinberg

2012 ◽  
Vol 187 (2) ◽  
pp. 630-637 ◽  
Author(s):  
Andrew J. Hung ◽  
Mukul B. Patil ◽  
Pascal Zehnder ◽  
Jie Cai ◽  
Casey K. Ng ◽  
...  

2017 ◽  
Vol 131 (11) ◽  
pp. 1010-1016 ◽  
Author(s):  
K Luu ◽  
L Straatman ◽  
D Nakku ◽  
B Westerberg ◽  
N Carter ◽  
...  

AbstractObjective:The provision of healthcare education in developing countries is a complex problem that simulation has the potential to help. This study aimed to evaluate the effectiveness of a low-cost ear surgery simulator, the Ear Trainer.Methods:The Ear Trainer was assessed in two low-resource environments in Cambodia and Uganda. Participants were video-recorded performing four specific middle-ear procedures, and blindly scored using a validated measurement tool. Face validity, construct validity and objective learning were assessed.Results:The Ear Trainer provides a realistic representation of the ear. Construct validity assessment confirmed that experts performed better than novices. Participants displayed improvement in all tasks except foreign body removal, likely because of a ceiling effect.Conclusion:This study validates the Ear Trainer as a useful training tool for otological microsurgical skills in developing world settings.


2014 ◽  
Vol 20 (1) ◽  
pp. 48-51 ◽  
Author(s):  
Patrick Culligan ◽  
Emil Gurshumov ◽  
Christa Lewis ◽  
Jennifer Priestley ◽  
Jodie Komar ◽  
...  

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