Jaden D. Barfuss , Fábio A. Nascimento , Erik Duhaime , Srishti Kapur , Ioannis Karakis , Marcus Ng , Aline Herlopian , Alice Lam , Douglas Maus , Jonathan J. Halford , Sydney Cash , M. Brandon Westover , Jin Jing
{"title":"通过竞争按需脑电图教育-一种新颖的，基于应用程序的方法来学习识别间歇癫痫样放电","authors":"Jaden D. Barfuss , Fábio A. Nascimento , Erik Duhaime , Srishti Kapur , Ioannis Karakis , Marcus Ng , Aline Herlopian , Alice Lam , Douglas Maus , Jonathan J. Halford , Sydney Cash , M. Brandon Westover , Jin Jing","doi":"10.1016/j.cnp.2023.08.003","DOIUrl":null,"url":null,"abstract":"<div><h3>Objective</h3><p>Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG.</p></div><div><h3>Methods</h3><p>We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings.</p></div><div><h3>Results</h3><p>Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice.</p></div><div><h3>Conclusions</h3><p>Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback.</p></div><div><h3>Significance</h3><p>This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.</p></div>","PeriodicalId":45697,"journal":{"name":"Clinical Neurophysiology Practice","volume":"8 ","pages":"Pages 177-186"},"PeriodicalIF":2.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480673/pdf/","citationCount":"0","resultStr":"{\"title\":\"On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges\",\"authors\":\"Jaden D. 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We additionally analyzed IED features that best correlated with expert ratings.</p></div><div><h3>Results</h3><p>Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice.</p></div><div><h3>Conclusions</h3><p>Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback.</p></div><div><h3>Significance</h3><p>This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.</p></div>\",\"PeriodicalId\":45697,\"journal\":{\"name\":\"Clinical Neurophysiology Practice\",\"volume\":\"8 \",\"pages\":\"Pages 177-186\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10480673/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Clinical Neurophysiology Practice\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2467981X23000240\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"NEUROSCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Clinical Neurophysiology Practice","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2467981X23000240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"NEUROSCIENCES","Score":null,"Total":0}
On-demand EEG education through competition – A novel, app-based approach to learning to identify interictal epileptiform discharges
Objective
Misinterpretation of EEGs harms patients, yet few resources exist to help trainees practice interpreting EEGs. We therefore sought to evaluate a novel educational tool to teach trainees how to identify interictal epileptiform discharges (IEDs) on EEG.
Methods
We created a public EEG test within the iOS app DiagnosUs using a pool of 13,262 candidate IEDs. Users were shown a candidate IED on EEG and asked to rate it as epileptiform (IED) or not (non-IED). They were given immediate feedback based on a gold standard. Learning was analyzed using a parametric model. We additionally analyzed IED features that best correlated with expert ratings.
Results
Our analysis included 901 participants. Users achieved a mean improvement of 13% over 1,000 questions and an ending accuracy of 81%. Users and experts appeared to rely on a similar set of IED morphologic features when analyzing candidate IEDs. We additionally identified particular types of candidate EEGs that remained challenging for most users even after substantial practice.
Conclusions
Users improved in their ability to properly classify candidate IEDs through repeated exposure and immediate feedback.
Significance
This app-based learning activity has great potential to be an effective supplemental tool to teach neurology trainees how to accurately identify IEDs on EEG.
期刊介绍：
Clinical Neurophysiology Practice (CNP) is a new Open Access journal that focuses on clinical practice issues in clinical neurophysiology including relevant new research, case reports or clinical series, normal values and didactic reviews. It is an official journal of the International Federation of Clinical Neurophysiology and complements Clinical Neurophysiology which focuses on innovative research in the specialty. It has a role in supporting established clinical practice, and an educational role for trainees, technicians and practitioners.
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