Ali Ghanem1, Dale J Podolsky2,3, David M Fisher4, Karen W Wong Riff4, Simon Myers1, James M Drake3,5, Christopher R Forrest4. 1. 1 Barts and The London School of Medicine and Dentistry, Blizard Institute, London, United Kingdom. 2. 2 Division of Plastic and Reconstructive Surgery, University of Toronto, Toronto, Ontario, Canada. 3. 3 Center for Image Guided Innovation and Therapeutic Intervention (CIGITI), Toronto, Ontario, Canada. 4. 4 Division of Plastic and Reconstructive Surgery, The Hospital for Sick Children, Toronto, Ontario, Canada. 5. 5 Division of Neurosurgery, The Hospital for Sick Children, Toronto, Ontario, Canada.
Abstract
OBJECTIVE: The objectives of this study were to assess economy of hand motion of residents, fellows, and staff surgeons using a high-fidelity cleft palate simulator to (1) stratify performance for the purpose of simulator validation and (2) to estimate the learning curve. DESIGN: Two residents, 2 fellows, and 2 staff surgeons performed cleft palate surgery on a high-fidelity cleft palate simulator while their hand motion was tracked using an electromagnetic hand sensor. The time, number of hand movements, and path length of their hands were determined for 10 steps of the procedure. The magnitude of these metrics was compared among the 3 groups of participants and utilized to estimate the learning curve using curve-fitting analysis. RESULTS: The residents required the most time, number of hand movements, and path length to complete the procedure. Although the number of hand movements was closely matched between the fellows and staff, the overall total path length was shorter for the staff. Inverse curves were fit to the data to represent the learning curve and 25 and 113 simulation sessions are required to reach within 5% and 1% of the expert level, respectively. CONCLUSION: The simulator successfully stratified performance using economy of hand motion. Path length is better matched to previous level of experience compared to time or number of hand movements.
OBJECTIVE: The objectives of this study were to assess economy of hand motion of residents, fellows, and staff surgeons using a high-fidelity cleft palate simulator to (1) stratify performance for the purpose of simulator validation and (2) to estimate the learning curve. DESIGN: Two residents, 2 fellows, and 2 staff surgeons performed cleft palate surgery on a high-fidelity cleft palate simulator while their hand motion was tracked using an electromagnetic hand sensor. The time, number of hand movements, and path length of their hands were determined for 10 steps of the procedure. The magnitude of these metrics was compared among the 3 groups of participants and utilized to estimate the learning curve using curve-fitting analysis. RESULTS: The residents required the most time, number of hand movements, and path length to complete the procedure. Although the number of hand movements was closely matched between the fellows and staff, the overall total path length was shorter for the staff. Inverse curves were fit to the data to represent the learning curve and 25 and 113 simulation sessions are required to reach within 5% and 1% of the expert level, respectively. CONCLUSION: The simulator successfully stratified performance using economy of hand motion. Path length is better matched to previous level of experience compared to time or number of hand movements.
Entities:
Keywords:
hard palate; palatoplasty; soft palate; surgical technique
Authors: Rami S Kantar; Allyson R Alfonso; Elie P Ramly; J Rodrigo Diaz-Siso; Corstiaan C Breugem; Roberto L Flores Journal: Plast Reconstr Surg Glob Open Date: 2019-09-23