Representation of neuro-oncology in American neurology departments: An analysis of Grand Rounds and perspectives from faculty and residents.
Elizabeth C Neil, Aubree Bruhnding, Zubair A Ahmed, Priya U Kumthekar, Nicholas A Blondin, Christian Grommes, Chirag B Patel, Sigmund Hsu, Feng Wang, Hongyu Miao, Noah L Rosen, Rashmi B Halker Singh, Mark J Burish, Zachary A Corbin
Abstract
Open AccessBackground: Neuro-oncology education in American neurology departments is highly variable, with no clear guidelines on appropriate practices. To better understand its current state, we focus on the perspective of departmental Grand Rounds (GR), views of adult neurology residency program directors (ANRPD), neuro-oncologists, and residents. Methods: First, we obtained GR series from academic years 2017-2018 and 2018-2019 via an online search and direct emails to neurology residency programs. Second, two online surveys were dispensed to ANRPD and neuro-oncologists. Third, a cohort of neurology residents completed surveys with pre/post-didactic tests. Results: Neuro-oncology consisted of 7% (28/ 411) GR in 2017-2018 and 6% (29/463) in 2018-2019; approximately 20% of institutions had no neuro-oncology GR. Twenty neuro-oncologists and 25 of the 175 ANRPDs completed the surveys. Respondents thought 1-to-4 GR annually was adequate. Thirty-five residents completed the survey. Residents might consider neuro-oncology with more exposure (77%, 27/35) and a stronger knowledge base (57%, 20/35). After 8-hours of didactics, residents demonstrated significant (P = .019) pre-/post-test improvement. Conclusions: Here, we begin to define the status of several aspects of neuro-oncology education: 1 in 5 academic institutions do not have any neuro-oncology GR lectures annually, and both ANRPDs and neuro-oncologists agree that 1-to-4 lectures annually are adequate. Residents overwhelmingly stated they would be more interested in neuro-oncology with more exposure, and a pilot study of 8 hours of didactic did show knowledge improvement. While these initial datasets require confirmation in larger studies, it suggests that small changes to the current state at some institutions could have a meaningful impact.