Investigation of Neurophobia amongst North American Veterinary Students and Development of a Veterinary Neurophobia Scoring Tool (VetNeuroQ)

“Neurophobia” is a phenomenon in human medical education where students develop negative attitudes towards neurology, impeding student learning and future clinical practice. While suspected to exist in veterinary medical education, it remains unstudied. The main objectives of this study were to examine North American veterinary student attitudes towards neurology and neurology education and explore elements that might contribute to neurophobia. Additional objectives were to evaluate veterinary educators’ perceptions of student neurophobia and to develop and validate a scoring tool (VetNeuroQ) to quantify veterinary neurophobia. Veterinary students and faculty at North American veterinary schools were surveyed. A scoring tool was developed from a subset of questions and validated using confirmatory factor analysis. Six hundred six anonymous responses were collected from students at all stages of veterinary education. Neurology training was reported as insufficient by 35.9% and most respondents perceived neurology to not be easy to learn. Neuroanatomy/physiology and neurolocalization were considered difficult concepts. Students rated low confidence in neurology (vs. other topics), and low interest in the Neurology/Neurosurgery specialty. 61.7% of educators reported neurophobia amongst their students. The proposed VetNeuroQ scale showed high reliability (Cronbach's alpha >0.7) and validity ( p < .05; CFI >0.9, RMSEA <0.08). VetNeuroQ scores were low but improved over the course of veterinary education. These findings demonstrate low self-efficacy, interest, and confidence, along with perceptions of difficulty, amongst veterinary students, consistent with neurophobia. Contributing elements are discussed. The VetNeuroQ scale allows quantification of veterinary student neurophobia and may be useful for screening students and assessing the impact of educational interventions

Integration of Geriatric Education Within the American Board of Emergency Medicine Model

Background: Emergency medicine (EM) resident training is guided by the American Board of Emergency Medicine Model of the Clinical Practice of Emergency Medicine (EM Model) and the EM Milestones as developed based on the knowledge, skills, and abilities (KSA) list. These are consensus documents developed by a collaborative working group of seven national EM organizations. External experts in geriatric EM also developed competency recommendations for EM residency education in geriatrics, but these are not being taught in many residency programs. Our objective was to evaluate how the geriatric EM competencies integrate/overlap with the EM Model and KSAs to help residency programs include them in their educational curricula.Methods: Trained emergency physicians independently mapped the geriatric resident competencies onto the 2019 EM Model items and the 2021 KSAs using Excel spreadsheets. Discrepancies were resolved by an independent reviewer with experience with the EM Model development and resident education, and the final mapping was reviewed by all team members.Results: The EM Model included 77% (20/26) of the geriatric competencies. The KSAs included most of the geriatric competencies (81%, 21/26). All but one of the geriatric competencies mapped onto either the EM Model or the KSAs. Within the KSAs, most of the geriatric competencies mapped onto necessary level skills (ranked B, C, D, or E) with only five (8%) also mapping onto advanced skills (ranked A).Conclusion: All but one of the geriatric EM competencies mapped to the current EM Model and KSAs. The geriatric competencies correspond to knowledge at all levels of training within the KSAs, from beginner to expert in EM. Educators in EM can use this mapping to integrate the geriatric competencies within their curriculums

Collaborative Approaches to Teaching and Building Visual Literacies

In “Collaborative Approaches to Teaching and Building Visual Literacies,” [Lopez et al.] give examples and learner perspectives that highlight many phases of creation.... The authors demonstrate how visual literacy learning benefits from the inclusion of a variety of perspectives in creative and collaborative environments." -Sara Schumache

Chronic GLP1 therapy reduces postprandial IL6 in obese humans with prediabetes.

Single-dose glucagon-like peptide 1 (GLP1) therapy increases postprandial plasma IL6 levels in prediabetic, obese humans. GLP1-IL6 interactions underly multiple antidiabetic effects, but these may differ after acute versus chronic therapy. This study examines postprandial effects of GLP1 after chronic therapy. Seven humans (six Black) with prediabetes and obesity completed 6 weeks of exenatide extended release therapy. Then subjects returned for pre- and post-meal measurements of plasma IL6, GLP1, glucagon, and related inflammatory markers. Weight, which was measured before and after therapy, did not change. Plasma IL6 decreased from baseline to postmeal state ( = 0.016), with decreases in free fatty acids (P < 0.001) and increases in insulin (P = 0.002), glucose (P < 0.0001), triglycerides (P = 0.0178), and glucagon (P = 0.018). Baseline GLP1 levels matched 6 weeks of therapy. The fall in postprandial plasma IL6, which contrasts with the increase after acute therapy, highlights the need for more investigation regarding the mechanisms of acute versus chronic GLP1-IL6 signaling

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1‐ and T2‐weighted MRI: Comparative analysis using water–fat MRI

BackgroundParaspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice.MethodsTo facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined.ResultsWe found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength.ConclusionWe conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest

Artificial intelligence-assisted quantification and assessment of whole slide images for pediatric kidney disease diagnosis.

MOTIVATION: Pediatric kidney disease is a widespread, progressive condition that severely impacts growth and development of children. Chronic kidney disease is often more insidious in children than in adults, usually requiring a renal biopsy for diagnosis. Biopsy evaluation requires copious examination by trained pathologists, which can be tedious and prone to human error. In this study, we propose an artificial intelligence (AI) method to assist pathologists in accurate segmentation and classification of pediatric kidney structures, named as AI-based Pediatric Kidney Diagnosis (APKD). RESULTS: We collected 2935 pediatric patients diagnosed with kidney disease for the development of APKD. The dataset comprised 93 932 histological structures annotated manually by three skilled nephropathologists. APKD scored an average accuracy of 94% for each kidney structure category, including 99% in the glomerulus. We found strong correlation between the model and manual detection in detected glomeruli (Spearman correlation coefficient r = 0.98, P < .001; intraclass correlation coefficient ICC = 0.98, 95% CI = 0.96-0.98). Compared to manual detection, APKD was approximately 5.5 times faster in segmenting glomeruli. Finally, we show how the pathological features extracted by APKD can identify focal abnormalities of the glomerular capillary wall to aid in the early diagnosis of pediatric kidney disease. AVAILABILITY AND IMPLEMENTATION: https://github.com/ChunyueFeng/Kidney-DataSet

Efficacy of Smoothing Algorithms to Enhance Detection of Visual Field Progression in Glaucoma.

PURPOSE: To evaluate and compare the effectiveness of nearest neighbor (NN)- and variational autoencoder (VAE)-smoothing algorithms to reduce variability and enhance the performance of glaucoma visual field (VF) progression models. DESIGN: Longitudinal cohort study. SUBJECTS: 7150 eyes (4232 patients), with ≥ 5 years of follow-up and ≥ 6 visits. METHODS: Vsual field thresholds were smoothed with the NN and VAE algorithms. The mean total deviation (mTD) and VF index rates, pointwise linear regression (PLR), permutation of PLR (PoPLR), and the glaucoma rate index were applied to the unsmoothed and smoothed data. MAIN OUTCOME MEASURES: The proportion of progressing eyes and the conversion to progression were compared between the smoothed and unsmoothed data. A simulation series of noiseless VFs with various patterns of glaucoma damage was used to evaluate the specificity of the smoothing models. RESULTS: The mean values of age and follow-up time were 62.8 (standard deviation: 12.6) years and 10.4 (standard deviation: 4.7) years, respectively. The proportion of progression was significantly higher for the NN and VAE smoothed data compared with the unsmoothed data. VF progression occurred significantly earlier with both smoothed data compared with unsmoothed data based on mTD rates, PLR, and PoPLR methods. The ability to detect the progressing eyes was similar for the unsmoothed and smoothed data in the simulation data. CONCLUSIONS: Smoothing VF data with NN and VAE algorithms improves the signal-to-noise ratio for detection of change, results in earlier detection of VF progression, and could help monitor glaucoma progression more effectively in the clinical setting. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article