Background: In persons with multiple sclerosis (MS), the Expanded Disability Status Scale (EDSS) is the criterion standard for assessing disability, but its in-person nature constrains patient participation in research and clinical assessments.
Objective: The aim of this study was to develop and validate a scalable, electronic, unsupervised patient-reported EDSS (ePR-EDSS) that would capture MS-related disability across the spectrum of severity.
Methods: We enrolled 136 adult MS patients, split into a preliminary testing Cohort 1 ( = 50), and a validation Cohort 2 ( = 86), which was evenly distributed across EDSS groups. Each patient completed an ePR-EDSS either immediately before or after a MS clinician's Neurostatus EDSS evaluation.
Results: In Cohort 2, mean age was 50.6 years (range = 26-80) and median EDSS was 3.5 (interquartile range (IQR) = [1.5, 5.5]). The ePR-EDSS and EDSS agreed within 1-point for 86% of examinations; kappa for agreement within 1-point was 0.85 ( < 0.001). The correlation coefficient between the two measures was 0.91 (<0.001).
Discussion: The ePR-EDSS was highly correlated with EDSS, with good agreement even at lower EDSS levels. For clinical care, the ePR-EDSS could enable the longitudinal monitoring of a patient's disability. For research, it provides a valid and rapid measure across the entire spectrum of disability and permits broader participation with fewer in-person assessments.
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http://dx.doi.org/10.1177/1352458520968814 | DOI Listing |
Phys Med Biol
January 2025
Beijing institute of control and electronic technology, 51 Beilijia, Muxidi, Xicheng District, Beijing 100038, Beijing, 100038, CHINA.
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Institute for Imaging, Data and Communications (IDCOM), School of Engineering, University of Edinburgh, Edinburgh, EH9 3FB, UK.
Artifacts are a common problem in physiological time series collected from intensive care units (ICU) and other settings. They affect the quality and reliability of clinical research and patient care. Manual annotation of artifacts is costly and time-consuming, rendering it impractical.
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December 2024
Research group BioGeoOmics, Department of Environmental Analytical Chemistry, Helmholtz Centre for Environmental Research, UFZ, Leipzig 04318, Germany.
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View Article and Find Full Text PDFPLoS One
December 2024
National Key Laboratory of Optical Field Manipulation Science and Technology, Chinese Academy of Sciences, Chengdu, China.
Landmark detection is a common task that benefits downstream computer vision tasks. Current landmark detection algorithms often train a sophisticated image pose encoder by reconstructing the source image to identify landmarks. Although a well-trained encoder can effectively capture landmark information through image reconstruction, it overlooks the semantic relationships between landmarks.
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