Education Research: Competency-Based EEG Education

Fábio A. Nascimento, Hong Gao, Roohi Katyal, Rebecca Matthews, Samantha V. Yap, Stefan Rampp, William O. Tatum, Roy E. Strowd, Sándor Beniczky
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引用次数: 1

Abstract

We recently published expert consensus-based curricular objectives for routine EEG (rEEG) interpretation for adult and child neurology residents. In this study, we used this curriculum framework to develop and validate an online, competency-based, formative and summative rEEG examination for neurology residents.We developed an online rEEG examination consisting of a brief survey and 30 multiple-choice questions covering EEG learning objectives for neurology residents in 4 domains: normal, abnormal, normal variants, and artifacts. Each question contained a deidentified EEG image, displayed in 2 montages (bipolar and average), reviewed and optimized by the authors to address the learning objectives. Respondents reported their level of confidence (LOC, 5-point Likert scale) with identifying 4 categories of EEG findings independently: states of wakefulness/sleep, sleep structures, normal variants, and artifacts. Accuracy and item discrimination were calculated for each question and LOC for each category. The test was disseminated by the International League Against Epilepsy and shared on social media.Of 2,080 responses, 922 were complete. Respondents comprised clinical neurophysiologists/experts (n = 41), EEG/epilepsy clinical fellows (n = 211), EEG technologists (n = 128), attending neurologists (n = 111), adult neurology residents (n = 227), child neurology residents (n = 108), medical students (n = 24), attending non-neurologists (n = 18), and others (n = 54). Mean overall scores (95% CI) were 82% (77–86) (clinical neurophysiologists), 81% (79–83) (clinical fellows), and 72% (70–73) (adult and child neurology residents). Experts were more confident than clinical fellows in all categories but sleep structures. Experts and clinical fellows were more confident than residents in all 4 categories. Among residents, accuracy and LOC increased as a function of prior EEG weeks of training. Accuracy improved from 67% (baseline/no prior EEG training) to 77% (>12 prior EEG weeks). More than 8 weeks of EEG training was needed to reach accuracy comparable with clinical neurophysiologists on this rEEG examination. Increase in LOC was slower and less robust than increase in accuracy. All but 3 questions had a high discrimination index (>0.25).This online, competency-based rEEG examination, mapped to a published EEG curriculum, has excellent psychometrics and differentiates experienced EEG readers from adult and child neurology residents. This online tool has the potential to improve resident EEG education worldwide.
教育研究:基于胜任力的脑电图教育
背景和目的我们最近发布了基于专家共识的成人和儿童神经内科住院医师常规脑电图(rEEG)解释课程目标。在本研究中,我们使用该课程框架为神经内科住院医师开发并验证了一种基于能力、形成性和总结性的在线rEEG考试。方法:我们开发了一个在线rEEG测试,包括一个简短的调查和30个选择题,涵盖了神经内科住院医生在4个领域的EEG学习目标:正常、异常、正常变异和伪影。每个问题包含一个去识别的脑电图图像,以两个蒙太奇(双极和平均)显示,由作者审查和优化以解决学习目标。受访者报告了他们的自信水平(LOC, 5点李克特量表),并独立确定了4类脑电图结果:清醒/睡眠状态、睡眠结构、正常变异和伪影。对每个问题和每个类别的LOC计算准确性和项目区分度。该测试由国际抗癫痫联盟发布,并在社交媒体上分享。结果在2080份回复中,922份完成。受访者包括临床神经生理学家/专家(n = 41)、脑电图/癫痫临床研究员(n = 211)、脑电图技术专家(n = 128)、神经内科主治医师(n = 111)、成人神经内科住院医师(n = 227)、儿童神经内科住院医师(n = 108)、医学生(n = 24)、非神经内科住院医师(n = 18)和其他(n = 54)。平均总得分(95% CI)为82%(77-86)(临床神经生理学家),81%(79-83)(临床研究员)和72%(70-73)(成人和儿童神经内科住院医师)。除了睡眠结构,专家在所有方面都比临床研究员更有信心。专家和临床研究员在这四个方面都比住院医生更有信心。在住院医师中,准确性和LOC随着先前EEG训练周数的增加而增加。准确率从67%(基线/未接受脑电图训练)提高到77%(12个脑电图周)。需要8周以上的脑电图训练才能达到与临床神经生理学家在rEEG检查上相当的准确性。LOC的增加比精度的增加更慢,更不稳健。除3个问题外,其余问题的歧视指数均较高(>0.25)。这个在线的,基于能力的rEEG考试,与已出版的EEG课程相匹配,具有出色的心理测量学,并将有经验的EEG读者与成人和儿童神经内科住院医生区分开来。这个在线工具有潜力改善世界范围内的居民脑电图教育。
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