Andrés M Bur1, Ernest D Gomez2, Jason G Newman2, Gregory S Weinstein2, Bert W O'Malley2, Christopher H Rassekh2, Katherine J Kuchenbecker3. 1. Department of Otolaryngology-Head and Neck Surgery, University of Kansas, Kansas City, Kansas, U.S.A. 2. Department of Otorhinolaryngology-Head and Neck Surgery, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A. 3. Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania, Philadelphia, Pennsylvania, U.S.A.
Abstract
OBJECTIVES/HYPOTHESIS: To develop and evaluate a high-fidelity training simulator for transoral robotic posterior hemiglossectomy. STUDY DESIGN: Prospective observational study. METHODS: We constructed a transoral robotic surgery (TORS) simulator using porcine tongue in a modified airway mannequin. Twenty-nine surgeons performed transoral robotic posterior hemiglossectomy on the simulator. The 20 resident subjects completed six trials each, and the five fellows and four attending surgeons completed two trials each. In addition to instrument vibrations, surgical video was recorded for each trial and was blindly rated using the Global Evaluative Assessment of Robotic Skill (GEARS), a validated instrument for assessing robotic surgical skill. RESULTS: Attending surgeons were faster (P = .004) and demonstrated greater technical skill than fellows or residents (P < .001). Resident completion time generally decreased over the study, becoming significantly faster by the fifth trial (P = .02). A similar trend was seen in resident GEARS scores, which generally increased and were significantly improved by the fourth trial (P = .008). Instrument vibrations were not significantly different between subject groups. Finally, subjects highly rated the realism and training value of the TORS simulator (mean 4.4 and 4.7 out of 5, respectively). CONCLUSIONS: The reported findings support using the described simulator as a training tool for TORS. Residents significantly improved in speed and technical skill over the course of six trials but did not achieve the performance levels of attending surgeons. These results demonstrate that high-fidelity simulation is a valuable tool for training novice surgeons in transoral robotic surgery. LEVEL OF EVIDENCE: NA. Laryngoscope, 127:2790-2795, 2017.
OBJECTIVES/HYPOTHESIS: To develop and evaluate a high-fidelity training simulator for transoral robotic posterior hemiglossectomy. STUDY DESIGN: Prospective observational study. METHODS: We constructed a transoral robotic surgery (TORS) simulator using porcine tongue in a modified airway mannequin. Twenty-nine surgeons performed transoral robotic posterior hemiglossectomy on the simulator. The 20 resident subjects completed six trials each, and the five fellows and four attending surgeons completed two trials each. In addition to instrument vibrations, surgical video was recorded for each trial and was blindly rated using the Global Evaluative Assessment of Robotic Skill (GEARS), a validated instrument for assessing robotic surgical skill. RESULTS: Attending surgeons were faster (P = .004) and demonstrated greater technical skill than fellows or residents (P < .001). Resident completion time generally decreased over the study, becoming significantly faster by the fifth trial (P = .02). A similar trend was seen in resident GEARS scores, which generally increased and were significantly improved by the fourth trial (P = .008). Instrument vibrations were not significantly different between subject groups. Finally, subjects highly rated the realism and training value of the TORS simulator (mean 4.4 and 4.7 out of 5, respectively). CONCLUSIONS: The reported findings support using the described simulator as a training tool for TORS. Residents significantly improved in speed and technical skill over the course of six trials but did not achieve the performance levels of attending surgeons. These results demonstrate that high-fidelity simulation is a valuable tool for training novice surgeons in transoral robotic surgery. LEVEL OF EVIDENCE: NA. Laryngoscope, 127:2790-2795, 2017.
Authors: Beiqun Zhao; Jenny Lam; Hannah M Hollandsworth; Arielle M Lee; Nicole E Lopez; Benjamin Abbadessa; Samuel Eisenstein; Bard C Cosman; Sonia L Ramamoorthy; Lisa A Parry Journal: Surg Endosc Date: 2019-07-08 Impact factor: 4.584
Authors: Babak Givi; Michael G Moore; Arnaud F Bewley; Charles S Coffey; Marc A Cohen; Amy C Hessel; Scharukh Jalisi; Steven Kang; Jason G Newman; Liana Puscas; Maisie Shindo; Andrew Shuman; Punam Thakkar; Donald T Weed; Ara Chalian Journal: Head Neck Date: 2020-05-08 Impact factor: 3.147
Authors: Kevin J Kovatch; Aileen P Wertz; Taylor R Carle; Rebecca S Harvey; Lauren A Bohm; Marc C Thorne; Kelly M Malloy Journal: OTO Open Date: 2019-04-26