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

3 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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