Background The increasing shift toward a more generalized medical undergraduate curriculum has led to limited exposure to subspecialties, including neurosurgery. The lack of standardized teaching may result in insufficient coverage of core learning outcomes. Social media (SoMe) in medical education are becoming an increasingly accepted and popular way for students to meet learning objectives outside formal medical school teaching. We delivered a series of case-based discussions (CbDs) over SoMe to attempt to meet core learning needs in neurosurgery and determine whether SoMe-based CbDs were an acceptable method of education. Methods Twitter was used as a medium to host 9 CbDs pertaining to common neurosurgical conditions in practice. A sequence of informative and interactive tweets were formulated before live CbDs and tweeted in progressive order. Demographic data and participant feedback were collected. Results A total of 277 participants were recorded across 9 CbDs, with 654,584 impressions generated. Feedback responses were received from 135 participants (48.7%). Participants indicated an increase of 77% in their level of knowledge after participating. Of participants, 57% ( n = 77) had previous CbD experience as part of traditional medical education, with 62% ( n = 84) receiving a form of medical education previously through SoMe. All participants believed that the CbDs objectives were met and would attend future sessions. Of participants, 99% ( n = 134) indicated that their expectations were met. Conclusions SoMe has been shown to be a favorable and feasible medium to host live, text-based interactive CbDs. SoMe is a useful tool for teaching undergraduate neurosurgery and is easily translatable to all domains of medicine and surgery.
Traumatic brain injury (TBI) remains a leading cause of death and disability worldwide. Motivations for outcome data collection in TBI are threefold: to improve patient outcomes, to facilitate research, and to provide the means and methods for wider injury surveillance. Such data play a pivotal role in population health, and ways to increase the reliability of data collection following TBI should be pursued. As a result, technology-aided follow-up of patients with neurotrauma is on the rise; there is, therefore, a need to describe how such technologies have been used. A scoping review was conducted and reported using the PRISMA extension (PRISMA-ScR). Five electronic databases (Embase, MEDLINE, Global Health, PsycInfo, and Scopus) were searched systematically using keywords derived from the concepts of “telemedicine,” “TBI,” “outcome assessment,” and “patient-generated health data.” Forty studies described follow-up technologies (FUTs) utilizing telephones (52.5%, n = 21), short message service (SMS; 10%, n = 4), smartphones (22.5%, n = 9), videoconferencing (10%, n = 4), digital assistants (2.5%, n = 1), and custom devices (2.5%, n = 1) among cohorts of patients with TBI of varying injury severity. Where reported, clinical facilitators, remote follow-up timing and intervals between sessions, synchronicity of follow-up instances, proxy involvement, outcome measures utilized, and technology evaluation efforts are described. FUTs can aid more temporally sensitive assessments and capture fluctuating sequelae, a benefit of particular relevance to TBI cohorts. However, the evidence base surrounding FUTs remains in its infancy, particularly with respect to large samples, low- and middle-income patient cohorts, and the validation of outcome measures for deployment via such remote technology.
The presence of interictal epileptiform discharges on electroencephalography (EEG) may indicate increased epileptic seizure risk and on invasive EEG are the signature of the irritative zone. In highly epileptogenic lesions -such as cortical tubers in tuberous sclerosis -these discharges can be recorded with intracranial stereotactic EEG as part of the evaluation for epilepsy surgery. Yet the network mechanisms that underwrite the generation and spread of these discharges remain poorly understood, limiting their current diagnostic use. Here, we investigate the dynamics of interictal epileptiform discharges using a combination of quantitative analysis of invasive EEG recordings and mesoscale neural mass modelling of cortical dynamics. We first characterise spatially organised local dynamics of discharges recorded from 36 separate tubers in 8 patients with tuberous sclerosis. We characterise these dynamics with a set of competing explanatory network models using dynamic causal modelling. Bayesian model comparison of plausible network architectures suggests that the recurrent coupling between neuronal populations within -and adjacent to -the tuber core explains the travelling wave dynamics observed in these patient recordings. Our results -based on interictal activity -unify competing theories about the pathological organisation of epileptic foci and surrounding cortex in patients with tuberous sclerosis. Coupled oscillator dynamics have previously been used to describe ictal activity, where fast travelling ictal discharges are commonly observed within the recruited seizure network. The interictal data analysed here add the insight that this functional architecture is already established in the interictal state. This links observations of interictal EEG abnormalities directly to pathological network coupling in epilepsy, with possible implications for epilepsy surgery approaches in tuberous sclerosis. Significance StatementInterictal epileptiform discharges (IEDs) are clinically important markers of an epileptic brain. Here we link local IED spread to network coupling through a combination of clinical recordings in paediatric patients with tuberous sclerosis complex, quantitative EEG analysis of interictal discharges spread, and Bayesian inference on coupled neural mass model parameters. We show that the kinds of interictal discharges seen in our patients require recurrent local network coupling extending beyond the putative seizure focus and that in fact only those recurrent coupled networks can support seizurelike and interictal dynamics when run in simulation. Our findings provide a novel integrated perspective on emergent epileptic dynamics in human patients.
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