The Cleft palate-craniofacial journal : official publication of the American Cleft Palate-Craniofacial Association · 2018
Economy of Hand Motion During Cleft Palate Surgery Using a High-Fidelity Cleft Palate Simulator.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Ghanem Ali, Podolsky Dale J, Fisher David M, Wong Riff Karen W, Myers Simon, Drake James M, Forrest Christopher R
Аннотация
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.
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.