Education Research: The current state of neurophysiology education in selected neurology residency programs
Objective Prior research has illustrated there is a knowledge gap in neurology residents' neurophysiology education (EEG and EMG), and we sought to understand whether this is still an issue and to recognize the barriers in order to create solutions and improve education. Methods Surveys were developed for adult neurology residents and one for program directors asking about confidence in neurophysiology knowledge, percent of graduates reaching level 4 ACGME (American Council of Graduate Medical Education) milestones in EEG and EMG, methods of learning used, interest in the subjects, and suggestions for improvements. Results Twenty-six program directors (19% responder rate) and 55 residents (from at least 16 different programs) completed the survey. Program directors thought that 85% of graduating residents met level 4 milestones in EEG and only 75% in EMG. Structured rotations and more time allocated to education of these topics were frequent barriers mentioned. Postgraduate year 4 residents were 60% and 67% confident in EEG and 64%, 59%, and 62.3% in EMG level 4 milestones. Learning to read EEGs was considered important throughout residents' training; however, this interest and value decreased over time with EMG. Conclusion In our study, program directors suspect up to a quarter of residents may graduate not meeting level 4 ACGME milestones, and residents expressed lack of confidence in these areas. The educational methods used to instruct residents in EEG and EMG were similar as were the barriers they face across programs. This information hopefully will help fuel curriculum design and interest in these important neurology techniques.
Interrater Reliability of Experts in Identifying Interictal Epileptiform Discharges in Electroencephalograms.
Importance
The validity of using electroencephalograms (EEGs) to diagnose epilepsy requires reliable detection of interictal epileptiform discharges (IEDs). Prior interrater reliability (IRR) studies are limited by small samples and selection bias.


Objective
To assess the reliability of experts in detecting IEDs in routine EEGs.


Design, Setting, and Participants
This prospective analysis conducted in 2 phases included as participants physicians with at least 1 year of subspecialty training in clinical neurophysiology. In phase 1, 9 experts independently identified candidate IEDs in 991 EEGs (1 expert per EEG) reported in the medical record to contain at least 1 IED, yielding 87 636 candidate IEDs. In phase 2, the candidate IEDs were clustered into groups with distinct morphological features, yielding 12 602 clusters, and a representative candidate IED was selected from each cluster. We added 660 waveforms (11 random samples each from 60 randomly selected EEGs reported as being free of IEDs) as negative controls. Eight experts independently scored all 13 262 candidates as IEDs or non-IEDs. The 1051 EEGs in the study were recorded at the Massachusetts General Hospital between 2012 and 2016.


Main Outcomes and Measures
Primary outcome measures were percentage of agreement (PA) and beyond-chance agreement (Gwet κ) for individual IEDs (IED-wise IRR) and for whether an EEG contained any IEDs (EEG-wise IRR). Secondary outcomes were the correlations between numbers of IEDs marked by experts across cases, calibration of expert scoring to group consensus, and receiver operating characteristic analysis of how well multivariate logistic regression models may account for differences in the IED scoring behavior between experts.


Results
Among the 1051 EEGs assessed in the study, 540 (51.4%) were those of females and 511 (48.6%) were those of males. In phase 1, 9 experts each marked potential IEDs in a median of 65 (interquartile range [IQR], 28-332) EEGs. The total number of IED candidates marked was 87 636. Expert IRR for the 13 262 individually annotated IED candidates was fair, with the mean PA being 72.4% (95% CI, 67.0%-77.8%) and mean κ being 48.7% (95% CI, 37.3%-60.1%). The EEG-wise IRR was substantial, with the mean PA being 80.9% (95% CI, 76.2%-85.7%) and mean κ being 69.4% (95% CI, 60.3%-78.5%). A statistical model based on waveform morphological features, when provided with individualized thresholds, explained the median binary scores of all experts with a high degree of accuracy of 80% (range, 73%-88%).


Conclusions and Relevance
This study's findings suggest that experts can identify whether EEGs contain IEDs with substantial reliability. Lower reliability regarding individual IEDs may be largely explained by various experts applying different thresholds to a common underlying statistical model.
Nietzsche's Moral Philosophy
Expounds and criticizes the moral consequences of Nietzsche's basic concept of the will to power. This study argues that Nietzsche's doctrines on moral subjects reveal a man profoundly torn between different and conflicting impulses.