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

2025· article· en· W4409786770 on OpenAlexaff
Noah S. Llaneras, R. W. Taylor, Justin K. Zhang, Jonah Orr, Kitty Y. Wu, Stahs Pripotnev, Susan E. Mackinnon

Bibliographic record

VenuePlastic & Reconstructive Surgery Global Open · 2025
Typearticle
Languageen
FieldComputer Science
TopicAI in cancer detection
Canadian institutionsSt. Joseph's Hospital
Fundersnot available
KeywordsInterpretation (philosophy)Computer scienceMultimediaArtificial intelligenceMathematics educationPsychologyProgramming language

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.313
Teacher spread0.300 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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