How Can We Improve the Delivery of Urology Teaching to Medical Students Using Modern Educational Techniques?

It is generally well-known that the medical school curriculum is becoming increasingly busy, more so with the COVID-19 pandemic. By itself, urology education will need to adapt to meet the changing circumstances, but it remains uncertain on how best to address this need. In this article, we will discuss several methods that will allow institutions to ease and overcome pressures using modern educational techniques. These methods can be classified based on the aspect of the curriculum they seek to improve, namely core-curricular teaching, anatomy training, virtual reality, and electronic learning opportunities. We anticipate that the implementation of these suggestions will enhance medical school teaching.

Time-frequency mapping of the rhythmic limb movements distinguishes convulsive epileptic from psychogenic nonepileptic seizures.

PURPOSE: A definite diagnosis of psychogenic nonepileptic seizures (PNES) usually requires in-patient video-electroencephalography (EEG) monitoring. Previous research has shown that convulsive psychogenic nonepileptic seizures (PNES) demonstrate a characteristic pattern of rhythmic movement artifact on the EEG. Herein we sought to examine the potential for time-frequency mapping of data from a movement-recording device (accelerometer) worn on the wrist as a diagnostic tool to differentiate between convulsive epileptic seizures and PNES. METHODS: Time-frequency mapping was performed on accelerometer traces obtained during 56 convulsive seizure-like events from 35 patients recorded during in-patient video-EEG monitoring. Twenty-six patients had PNES, eight had epileptic seizures, and one had both seizure types. The time-frequency maps were derived from fast Fourier transformations to determine the dominant frequency for sequential 2.56-s blocks for the course of each event. KEY FINDINGS: The coefficient of variation (CoV) of limb movement frequency for the PNES events was less than for the epileptic seizure events (median, 17.18% vs. 52.23%; p < 0.001). A blinded review of the time-frequency maps by an epileptologist was accurate in differentiating between the event types, that is, 38 (92.7%) of 41 and 6 (75%) of 8 nonepileptic and epileptic seizures, respectively, were diagnosed correctly, with seven events classified as "nondiagnostic." Using a CoV cutoff score of 32% resulted in similar classification accuracy, with 42 (93%) of 45 PNES and 10 (91%) of 11 epileptic seizure events correctly diagnosed. SIGNIFICANCE: Time-frequency analysis of data from a wristband movement monitor could be utilized as a diagnostic tool to differentiate between epileptic and nonepileptic convulsive seizure-like events.