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. doi abstract bibtex 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}
}
Downloads: 0
{"_id":"3vED3CewsXpgvs7Wi","bibbaseid":"frischknecht-kasten-hamstra-perkins-gillespie-armstrong-minter-theobjectiveassessmentofexpertsandnovicessuturingskillsusinganimageanalysisprogram-2013","downloads":0,"creationDate":"2017-03-07T13:13:11.330Z","title":"The Objective Assessment of Experts' and Novices' Suturing Skills Using An Image Analysis Program","author_short":["Frischknecht, A. C","Kasten, S. J","Hamstra, S. J","Perkins, N. C","Gillespie, R B.","Armstrong, T. J","Minter, R. M"],"year":2013,"bibtype":"article","biburl":"http://bibbase.org/zotero/remi.wolf","bibdata":{"bibtype":"article","type":"article","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":[{"propositions":[],"lastnames":["Frischknecht"],"firstnames":["Adam","C"],"suffixes":[]},{"propositions":[],"lastnames":["Kasten"],"firstnames":["Steven","J"],"suffixes":[]},{"propositions":[],"lastnames":["Hamstra"],"firstnames":["Stanley","J"],"suffixes":[]},{"propositions":[],"lastnames":["Perkins"],"firstnames":["Noel","C"],"suffixes":[]},{"propositions":[],"lastnames":["Gillespie"],"firstnames":["R","Brent"],"suffixes":[]},{"propositions":[],"lastnames":["Armstrong"],"firstnames":["Thomas","J"],"suffixes":[]},{"propositions":[],"lastnames":["Minter"],"firstnames":["Rebecca","M"],"suffixes":[]}],"month":"February","year":"2013","pmid":"23269303","pages":"260--264","bibtex":"@article{frischknecht_objective_2013,\n\ttitle = {The {Objective} {Assessment} of {Experts}' and {Novices}' {Suturing} {Skills} {Using} {An} {Image} {Analysis} {Program}},\n\tvolume = {88},\n\tissn = {1938-808X},\n\tdoi = {10.1097/ACM.0b013e31827c3411},\n\tabstract = {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.},\n\tnumber = {2},\n\tjournal = {Academic medicine: journal of the Association of American Medical Colleges},\n\tauthor = {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},\n\tmonth = feb,\n\tyear = {2013},\n\tpmid = {23269303},\n\tpages = {260--264}\n}\n\n","author_short":["Frischknecht, A. C","Kasten, S. J","Hamstra, S. J","Perkins, N. C","Gillespie, R B.","Armstrong, T. J","Minter, R. M"],"key":"frischknecht_objective_2013","id":"frischknecht_objective_2013","bibbaseid":"frischknecht-kasten-hamstra-perkins-gillespie-armstrong-minter-theobjectiveassessmentofexpertsandnovicessuturingskillsusinganimageanalysisprogram-2013","role":"author","urls":{},"downloads":0},"search_terms":["objective","assessment","experts","novices","suturing","skills","using","image","analysis","program","frischknecht","kasten","hamstra","perkins","gillespie","armstrong","minter"],"keywords":[],"authorIDs":[],"dataSources":["AvgwXzHNvzrPfaxbA"]}