Expert accuracy and inter-rater agreement of “must-know” EEG findings for adult and child neurology residents

Fábio A. Nascimento, Roohi Katyal, Marcia Olandoski, Hong Gao, Samantha Yap, Rebecca Matthews, Stefan Rampp, William Tatum, Roy Strowd, Sándor Beniczky

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Objective: We published a list of “must-know” routine EEG (rEEG) findings for trainees based on expert opinion. Here, we studied the accuracy and inter-rater agreement (IRA) of these “must-know” rEEG findings among international experts. Methods: A previously validated online rEEG examination was disseminated to EEG experts. It consisted of a survey and 30 multiple-choice questions predicated on the previously published “must-know” rEEG findings divided into four domains: normal, abnormal, normal variants, and artifacts. Questions contained de-identified 10–20-s epochs of EEG that were considered unequivocal examples by five EEG experts. Results: The examination was completed by 258 international EEG experts. Overall mean accuracy and IRA (AC1) were 81% and substantial (0.632), respectively. The domain-specific mean accuracies and IRA were: 76%, moderate (0.558) (normal); 78%, moderate (0.575) (abnormal); 85%, substantial (0.678) (normal variants); 85%, substantial (0.740) (artifacts). Academic experts had a higher accuracy than private practice experts (82% vs. 77%; p =.035). Country-specific overall mean accuracies and IRA were: 92%, almost perfect (0.836) (U.S.); 86%, substantial (0.762) (Brazil); 79%, substantial (0.646) (Italy); and 72%, moderate (0.496) (India). In conclusion, collective expert accuracy and IRA of “must-know” rEEG findings are suboptimal and heterogeneous. Significance: We recommend the development and implementation of pragmatic, accessible, country-specific ways to measure and improve the expert accuracy and IRA.

Original languageEnglish
Pages (from-to)109-120
Number of pages12
JournalEpileptic Disorders
Volume26
Issue number1
DOIs
StatePublished - Feb 2024

Keywords

  • EEG
  • EEG education
  • education
  • inter-rater agreement
  • inter-rater variability
  • noise

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