Diagnostic and classification criteria for Sjögren's syndrome (SS) continue to evolve as more is learned about SS and about autoimmune diseases in general. Among diagnostic or classification criteria for SS that are in current use, most include various and variable combinations of results from questions about symptoms and objective tests, many of which are not specific to SS. Given the rapid increase of genetic knowledge about other autoimmune diseases and the potential of finding and testing new biological agents to treat SS, selection of patients who have as uniform a disease process as possible becomes an important goal to better understand and treat this prevalent autoimmune disease. Such is the goal and promise of the latest entry into the SS classification criteria field.
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http://dx.doi.org/10.1016/j.lpm.2012.05.023 | DOI Listing |
PLoS One
January 2025
School of Optometry and Vision Science, UNSW Sydney, Sydney, New South Wales, Australia.
Purpose: In this study, we investigated the performance of deep learning (DL) models to differentiate between normal and glaucomatous visual fields (VFs) and classify glaucoma from early to the advanced stage to observe if the DL model can stage glaucoma as Mills criteria using only the pattern deviation (PD) plots. The DL model results were compared with a machine learning (ML) classifier trained on conventional VF parameters.
Methods: A total of 265 PD plots and 265 numerical datasets of Humphrey 24-2 VF images were collected from 119 normal and 146 glaucomatous eyes to train the DL models to classify the images into four groups: normal, early glaucoma, moderate glaucoma, and advanced glaucoma.
Objective: The aim of this study was to evaluate and validate the accuracy and performance characteristics of administrative codes in diagnosing autoinflammatory syndromes (AISs).
Methods: We identified potential AIS patients from the electronic medical records at the University of Iowa Hospital and Clinics and the Stead Family Children's Hospital using a screening filter based on the 10th edition of the International Classification of Diseases (ICD-10) codes and interleukin-1 antagonists. Diagnostic criteria for adult-onset Still disease, systemic juvenile idiopathic arthritis, Behçet disease (BD), familial Mediterranean fever (FMF), cryopyrin-associated periodic syndrome (CAPS), and SAPHO (synovitis, acne, pustulosis, hyperostosis, and osteitis) syndrome and chronic nonbacterial osteomyelitis (SAPHO-CNO) were reviewed for each patient.
Introduction: Publishing medical metadata stored in case report forms (CRFs) is a prerequisite for the development of a learning health system (LHS) by fostering reuse of metadata and standardization in health research. The aim of our study was to investigate medical researchers' (MRs) willingness to share CRFs, to identify reasons for and against CRF sharing, and to determine if and under which conditions MRs might consider sharing CRF metadata via a public registry.
Methods: We examined CRF data sharing commitments for 1842 interventional trials registered on the German Clinical Trials Registry (DRKS) from January 1, 2020, to December 31, 2021.
Soc Sci Humanit Open
June 2024
University of Washington, Bothell, USA.
The first seven months of the US COVID-19 pandemic saw a massive increase in COVID-19-related crowdfunding campaigns. Despite their popularity, these campaigns were rarely successful in reaching their monetary goals, with nearly 40% of them not receiving a single donation. Previous research has indicated that crowdfunding has increased inequities and disparities in wealth, and this study set out to examine the situation in Washington State, an area greatly divided socio-economically, culturally, and geographically.
View Article and Find Full Text PDFCureus
January 2025
General Practice, Wad Medani Hospital, Wad Medani, SDN.
To enhance patient outcomes in pediatric cancer, a better understanding of the medical and biological risk variables is required. With the growing amount of data accessible to research in pediatric cancer, machine learning (ML) is a form of algorithmic inference from sophisticated statistical techniques. In addition to highlighting developments and prospects in the field, the objective of this systematic study was to methodically describe the state of ML in pediatric oncology.
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