The Objective Assessment of Experts' and Novices' Suturing Skills Using An Image Analysis Program. Frischknecht, A. C, Kasten, S. J, Hamstra, S. J, Perkins, N. C, Gillespie, R B., Armstrong, T. J, & Minter, R. M Academic medicine: journal of the Association of American Medical Colleges, 88(2):260--264, February, 2013.
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PURPOSE: To objectively assess suturing performance using an image analysis program and to provide validity evidence for this assessment method by comparing experts' and novices' performance. METHOD: In 2009, the authors used an image analysis program to extract objective variables from digital images of suturing end products obtained during a previous study involving third-year medical students (novices) and surgical faculty and residents (experts). Variables included number of stitches, stitch length, total bite size, travel, stitch orientation, total bite-size-to-travel ratio, and symmetry across the incision ratio. The authors compared all variables between groups to detect significant differences and two variables (total bite-size-to-travel ratio and symmetry across the incision ratio) to ideal values. RESULTS: Five experts and 15 novices participated. Experts' and novices' performances differed significantly (P \textless .05) with large effect sizes attributable to experience (Cohen d \textgreater 0.8) for total bite size (P = .009, d = 1.5), travel (P = .045, d = 1.1), total bite-size-to-travel ratio (P \textless .0001, d = 2.6), stitch orientation (P = .014,d = 1.4), and symmetry across the incision ratio (P = .022, d = 1.3). CONCLUSIONS: The authors found that a simple computer algorithm can extract variables from digital images of a running suture and rapidly provide quantitative summative assessment feedback. The significant differences found between groups confirm that this system can discriminate between skill levels. This image analysis program represents a viable training tool for objectively assessing trainees' suturing, a foundational skill for many medical specialties.
@article{frischknecht_objective_2013,
	title = {The {Objective} {Assessment} of {Experts}' and {Novices}' {Suturing} {Skills} {Using} {An} {Image} {Analysis} {Program}},
	volume = {88},
	issn = {1938-808X},
	doi = {10.1097/ACM.0b013e31827c3411},
	abstract = {PURPOSE: To objectively assess suturing performance using an image analysis program and to provide validity evidence for this assessment method by comparing experts' and novices' performance. METHOD: In 2009, the authors used an image analysis program to extract objective variables from digital images of suturing end products obtained during a previous study involving third-year medical students (novices) and surgical faculty and residents (experts). Variables included number of stitches, stitch length, total bite size, travel, stitch orientation, total bite-size-to-travel ratio, and symmetry across the incision ratio. The authors compared all variables between groups to detect significant differences and two variables (total bite-size-to-travel ratio and symmetry across the incision ratio) to ideal values. RESULTS: Five experts and 15 novices participated. Experts' and novices' performances differed significantly (P {\textless} .05) with large effect sizes attributable to experience (Cohen d {\textgreater} 0.8) for total bite size (P = .009, d = 1.5), travel (P = .045, d = 1.1), total bite-size-to-travel ratio (P {\textless} .0001, d = 2.6), stitch orientation (P = .014,d = 1.4), and symmetry across the incision ratio (P = .022, d = 1.3). CONCLUSIONS: The authors found that a simple computer algorithm can extract variables from digital images of a running suture and rapidly provide quantitative summative assessment feedback. The significant differences found between groups confirm that this system can discriminate between skill levels. This image analysis program represents a viable training tool for objectively assessing trainees' suturing, a foundational skill for many medical specialties.},
	number = {2},
	journal = {Academic medicine: journal of the Association of American Medical Colleges},
	author = {Frischknecht, Adam C and Kasten, Steven J and Hamstra, Stanley J and Perkins, Noel C and Gillespie, R Brent and Armstrong, Thomas J and Minter, Rebecca M},
	month = feb,
	year = {2013},
	pmid = {23269303},
	pages = {260--264}
}
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