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http://dx.doi.org/10.1212/WNL.0000000000001429 | DOI Listing |
J Urol
January 2025
Division of Urology, Department of Surgery, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA.
Purpose: Urinary incontinence (UI) is common in nulliparous female elite athletes, but underlying pathophysiology is inadequately understood. We examined urinary symptoms and associated pelvic floor anatomy and function in this population, hypothesizing that athletes with UI would exhibit pelvic floor findings seen in older incontinent women (e.g.
View Article and Find Full Text PDFStroke
February 2025
Division of Interventional Neuroradiology, Department of Radiology (H.C., S.M., D.G.), University of Maryland Medical Center, Baltimore.
Background: Sex-specific differences in stroke risk factors, clinical presentation, and outcomes are well documented. However, little is known about real-world differences in transient ischemic attack (TIA) hospitalizations and outcomes between men and women.
Methods: This was a retrospective cohort study of the 2016 to 2021 Nationwide Readmissions Database in the United States.
Circulation
January 2025
Divisions of Cardiac Surgery (H.T., A.Q., R.E., R.V., M.M., J.H.C., S.V.), Li Ka Shing Knowledge Institute, St. Michael's Hospital of Unity Health Toronto, Ontario, Canada.
PLoS One
January 2025
Department of Business Economics and Management, Masaryk University Faculty of Economics and Administration, Brno, Czech Republic.
The subject of this paper is modeling customer satisfaction in the mobile telecommunication industry following the Covid-19 pandemic. Based on standard customer satisfaction models, a specialized model tailored for the mobile telecommunication industry has been developed to account for its unique characteristics, including market concentration. This model was created within the Slovakian context using the Structural Equation Modelling method.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America.
Introduction: Benign and malignant myxoid soft tissue tumors have shared clinical, imaging, and histologic features that can make diagnosis challenging. The purpose of this study is comparison of the diagnostic performance of a radiomic based machine learning (ML) model to musculoskeletal radiologists.
Methods: Manual segmentation of 90 myxoid soft tissue tumors (45 myxomas and 45 myxofibrosarcomas) was performed on axial T1, and T2FS or STIR magnetic resonance imaging sequences.
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