@article{fichtinger2019m,

title = {Surgery Tutor for computational assessment of technical proficiency in soft-tissue tumor resection in a simulated setting},

author = {Caitlin T Yeo and Justine Ring and Matthew S Holden and Tamas Ungi and Ayca Toprak and Gabor Fichtinger and Boris Zevin},

url = {https://www.sciencedirect.com/science/article/pii/S1931720418306135},

year  = {2019},

date = {2019-01-01},

journal = {Journal of Surgical Education},

volume = {76},

issue = {3},

pages = {872-880},

publisher = {Elsevier},

abstract = {Background 

In competency-based medical education, progression between milestones requires reliable and valid methods of assessment. Surgery Tutor is an open-source motion tracking platform developed to objectively assess technical proficiency during open soft-tissue tumor resections in a simulated setting. The objective of our study was to provide evidence in support of construct validity of the scores obtained by Surgery Tutor. We hypothesized that Surgery Tutor would discriminate between novice, intermediate, and experienced operators. 

Methods 

Thirty participants were assigned to novice, intermediate, or experienced groups, based on the number of prior soft-tissue resections performed. Each participant resected 2 palpable and 2 nonpalpable lesions from a soft-tissue phantom. Surgery Tutor was used to track hand and instrument motions, number of tumor breaches, and time to perform each resection …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}