144. A Video-based Learning Module in an Effective Way to Teach the Interpretation of Pre-operative Electrodiagnostic Studies

INTRODUCTION: Most plastic surgeons are not trained in the interpretation of electrodiagnostic studies (EDX), highlighting an opportunity for improved education in the diagnosis of nerve injuries and the determination of surgical candidacy. This study hypothesizes that a video learning module (VLM) is a feasible and efficacious method for teaching nerve surgeons to interpret EDX. METHODS: Participants were recruited from professional surgical societies and through a surgical education platform. The senior author (S.E.M.) developed the VLM, which consisted of a pre-lecture assessment, a 42-minute pre-recorded video lecture on interpreting EDX, and a post-lecture assessment. A retention assessment was distributed at three months follow-up. The lecture covered topics such as the classification of nerve injuries, components of nerve conduction studies, components of electromyography, and case studies elaborating on these principles. Participants were also given a module feedback survey. A Shapiro-Wilk test was performed to assess for normal distribution. Data found not to be normally distributed were analyzed using independent-samples Mann-Whitney U tests. RESULTS:119 participants completed the pre-lecture assessment, with a median score of 7 (range 0-12, IQR 5-9) out of a total of 12 possible points. Seventy participants (58.8%) completed the post-lecture knowledge assessment, with a median score of 9 (range 2-12, IQR 8-11). The two-point increase in median score between the pre-and post-lecture knowledge assessment was significant (p<0.001). Twenty-nine participants (24.4%) completed the three-month follow-up retention assessment, with a median score of 11 (range 4-12, IQR 9-12). The two-point increase in median score between the post-lecture and retention knowledge assessment was significant (p<0.025). Fifty-seven participants completed the module feedback survey; participants rated their overall satisfaction with the video module on a scale of 0 to 10, with a median score of 9 (range 6-10, IQR 8-10). 77.2% of participants reported that the length of the video module was “perfect,” and 86% anticipated incorporating the material into their clinical practice. CONCLUSION: This study demonstrates that a video learning module effectively teaches nerve surgeons to interpret electrodiagnostic studies, as evidenced by significant improvements in post-lecture and 3-month retention scores. High participant satisfaction highlights the potential of this format for broadly disseminating surgical education. Study design of the surgical video module.

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Publication Details

Journal
Plastic & Reconstructive Surgery Global Open
Published
2025-04-24
DOI
https://doi.org/10.1097/01.gox.0001112524.79751.30
Primary Topic
AI in cancer detection
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article
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article

144. A Video-based Learning Module in an Effective Way to Teach the Interpretation of Pre-operative Electrodiagnostic Studies

Jonah Orr, Susan E. Mackinnon, Kitty Y. Wu, Noah S. Llaneras et al.
Plastic & Reconstructive Surgery Global Open
AI in cancer detection
article

144. A Video-based Learning Module in an Effective Way to Teach the Interpretation of Pre-operative Electrodiagnostic Studies

Jonah Orr, Susan E. Mackinnon, Kitty Y. Wu, Noah S. Llaneras, Stahs Pripotnev, Justin K. Zhang, R. W. Taylor
article en

Abstract

INTRODUCTION: Most plastic surgeons are not trained in the interpretation of electrodiagnostic studies (EDX), highlighting an opportunity for improved education in the diagnosis of nerve injuries and the determination of surgical candidacy. This study hypothesizes that a video learning module (VLM) is a feasible and efficacious method for teaching nerve surgeons to interpret EDX. METHODS: Participants were recruited from professional surgical societies and through a surgical education platform. The senior author (S.E.M.) developed the VLM, which consisted of a pre-lecture assessment, a 42-minute pre-recorded video lecture on interpreting EDX, and a post-lecture assessment. A retention assessment was distributed at three months follow-up. The lecture covered topics such as the classification of nerve injuries, components of nerve conduction studies, components of electromyography, and case studies elaborating on these principles. Participants were also given a module feedback survey. A Shapiro-Wilk test was performed to assess for normal distribution. Data found not to be normally distributed were analyzed using independent-samples Mann-Whitney U tests. RESULTS:119 participants completed the pre-lecture assessment, with a median score of 7 (range 0-12, IQR 5-9) out of a total of 12 possible points. Seventy participants (58.8%) completed the post-lecture knowledge assessment, with a median score of 9 (range 2-12, IQR 8-11). The two-point increase in median score between the pre-and post-lecture knowledge assessment was significant (p<0.001). Twenty-nine participants (24.4%) completed the three-month follow-up retention assessment, with a median score of 11 (range 4-12, IQR 9-12). The two-point increase in median score between the post-lecture and retention knowledge assessment was significant (p<0.025). Fifty-seven participants completed the module feedback survey; participants rated their overall satisfaction with the video module on a scale of 0 to 10, with a median score of 9 (range 6-10, IQR 8-10). 77.2% of participants reported that the length of the video module was “perfect,” and 86% anticipated incorporating the material into their clinical practice. CONCLUSION: This study demonstrates that a video learning module effectively teaches nerve surgeons to interpret electrodiagnostic studies, as evidenced by significant improvements in post-lecture and 3-month retention scores. High participant satisfaction highlights the potential of this format for broadly disseminating surgical education. Study design of the surgical video module.

Plastic & Reconstructive Surgery Global OpenVol. 13(S1)
Washington University in St. Louis (US), University of Utah (US), Mayo Clinic in Arizona (US)
Quality Education
Openalex Percentile: Top 96%
AI in cancer detection
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