Best Practices Using Ex Vivo Animal Brain Models in Neurosurgical Education to Assess Surgical Expertise.

World Neurosurg

Neurosurgical Simulation and Artificial Intelligence Learning Centre, Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada.

Published: November 2021

Background: Ex vivo animal brain simulation models are being increasingly used for neurosurgical training because these models can replicate human brain conditions. The goal of the present report is to provide the neurosurgical community interested in using ex vivo animal brain simulation models with guidelines for comprehensively and rigorously conducting, documenting, and assessing this type of research.

Methods: In consultation with an interdisciplinary group of physicians and researchers involved in ex vivo models and a review of the literature on the best practices guidelines for simulation research, we developed the "ex vivo brain model to assess surgical expertise" (EVBMASE) checklist. The EVBMASE checklist provides a comprehensive quantitative framework for analyzing and reporting studies involving these models. We applied The EVBMASE checklist to the studies reported of ex vivo animal brain models to document how current ex vivo brain simulation models are used to train surgical expertise.

Results: The EVBMASE checklist includes defined subsections and a total score of 20, which can help investigators improve studies and provide readers with techniques to better assess the quality and any deficiencies of the research. We classified 18 published ex vivo brain models into modified (group 1) and nonmodified (group 2) models. The mean total EVBMASE score was 11 (55%) for group 1 and 4.8 (24.2%) for group 2, a statistically significant difference (P = 0.006) mainly attributed to differences in the simulation study design section (P = 0.003).

Conclusions: The present findings should help contribute to more rigorous application, documentation, and assessment of ex vivo brain simulation research.

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Source
http://dx.doi.org/10.1016/j.wneu.2021.08.061DOI Listing

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