A carbon footprint study of the Canadian medical residency interview tour.

BACKGROUND: Each spring, thousands of Canadian medical students travel across the country to interview for residency positions, a process known as the CaRMS tour. Despite the large scale of travel, the CaRMS tour has received little environmental scrutiny. PURPOSE: To estimate the national carbon footprint of flights associated with the CaRMS tour, as well as reductions in emissions achievable by transitioning to alternative models. METHODS: We developed a three-question online commuter survey to collect the unique travel itineraries of applicants in the 2020 CaRMS tour. We calculated the emissions associated with all flights and modelled expected emissions for two alternative in-person interview models, and two virtual interview models. RESULTS: We collected 960 responses out of 2943 applicants across all 17 Canadian medical schools. We calculated the carbon footprint of flights for the 2020 CaRMS as 4239 tCO2e (tonnes of carbon dioxide equivalents), averaging 1.44 tCO2e per applicant. The average applicant's tour emissions represent 35.1% of the average Canadian's annual household carbon footprint, and the emissions of 26.7% of respondents exceeded their entire annual '2050 carbon budget.' Centralized in-person interviews could reduce emissions by 13.7% to 74.7%, and virtual interviews by at least 98.4% to 99.9%. CONCLUSIONS: Mandatory in-person residency interviews in Canada contribute significant emissions and reflect a culture of emissions-intensive practices. Considerable decarbonization of the CaRMS tour is possible, and transitioning to virtual interviews could eliminate the footprint almost entirely.

Access to Focal Spasticity Care: A Cross Canada Survey of Physiatrists.

Successful management of focal spasticity requires access to botulinum toxin type A (BoNT-A) injections, physiotherapy, occupational therapy, and orthoses/bracing. To assess the quality of focal spasticity care across Canada, we sent a survey consisting of 22 questions to physiatrists involved in the management of outpatient spasticity. Thirty-four physiatrists from all 10 provinces responded to the survey. Wait time for BoNT-A treatment averaged 12.7 weeks from time of referral across Canada. More than 75% of patients faced barriers to obtaining physical therapy and orthoses. Access to best quality care for spasticity patients across Canada varies widely.

Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia.

Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.

Enhancing Neurosurgical Education in Low- and Middle-income Countries: Current Methods and New Advances.

Low- and middle-income countries (LMICs) face a critical shortage of basic surgical services. Adequate neurosurgical services can have a far-reaching positive impact on society's health care and, consequently, the economic development in LMICs. Yet surgery, and specifically neurosurgery has been a long neglected sector of global health. This article reviews the current efforts to enhance neurosurgery education in LMICs and outlines ongoing approaches for improvement. In addition, we introduce the concept of a sustainable and cost-effective model to enhance neurosurgical resources in LMICs and describe the process and methods of online curriculum development.

Multi-contrast submillimetric 3 Tesla hippocampal subfield segmentation protocol and dataset.

The hippocampus is composed of distinct anatomical subregions that participate in multiple cognitive processes and are differentially affected in prevalent neurological and psychiatric conditions. Advances in high-field MRI allow for the non-invasive identification of hippocampal substructure. These approaches, however, demand time-consuming manual segmentation that relies heavily on anatomical expertise. Here, we share manual labels and associated high-resolution MRI data (MNI-HISUB25; submillimetric T1- and T2-weighted images, detailed sequence information, and stereotaxic probabilistic anatomical maps) based on 25 healthy subjects. Data were acquired on a widely available 3 Tesla MRI system using a 32 phased-array head coil. The protocol divided the hippocampal formation into three subregions: subicular complex, merged Cornu Ammonis 1, 2 and 3 (CA1-3) subfields, and CA4-dentate gyrus (CA4-DG). Segmentation was guided by consistent intensity and morphology characteristics of the densely myelinated molecular layer together with few geometry-based boundaries flexible to overall mesiotemporal anatomy, and achieved excellent intra-/inter-rater reliability (Dice index ≥90/87%). The dataset can inform neuroimaging assessments of the mesiotemporal lobe and help to develop segmentation algorithms relevant for basic and clinical neurosciences.