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COVID-19 is a trigger of autoimmune rheumatic diseases: a hypothesis tested over time.
Rheumatol Int
December 2024
Department of General Practice N2, South Kazakhstan Medical Academy, Shymkent, Kazakhstan.
We discuss the paper recently published in Rheumatology Internationa. This article reflects on the prevalence of autoimmune rheumatic diseases (ARD) during the COVID-19 pandemic (2020-2023) and compares the same with the pre-pandemic period (2016-2019). We assume that SARS-CoV-2 triggers ARD.
View Article and Find Full Text PDFProstate-specific antigen testing in the United States during 2008-2022 in relation to the US preventive services task force recommendations.
Sci Rep
December 2024
Department of Public Health, College of Life Sciences, Brigham Young University, 2063 Life Sciences Building, Provo, UT, 84602, USA.
The prevalence of prostate-specific antigen (PSA) testing has consistently fallen for several years. This study explored how the decreasing trend differs by selected variables and reasons for taking the PSA test. Analyses involved men, aged 40 years or older, who completed the Behavior Risk Factor Surveillance System (BRFSS) survey in even number years from 2008 through 2022.
View Article and Find Full Text PDFThe Gift of Time, How Do I Want to Spend It? Exploring Preferences for Time Allocation Among Women with and without a Breast Cancer Diagnosis.
Appl Health Econ Health Policy
December 2024
Health Systems and Health Economics, School of Public Health, Curtin University, Bentley, Perth, Australia.
Background: Women's preferences for time allocation reveal how they would like to prioritise market work, family life, and other competing activities. Whilst preferences may not always directly translate to behaviour, they are an important determinant of intention to act.
Objective: We present the first study to apply a discrete choice experiment (DCE) to investigate time allocation preferences among women diagnosed with breast cancer and women without a cancer diagnosis.
Explainable artificial intelligence for stroke prediction through comparison of deep learning and machine learning models.
Sci Rep
December 2024
Artificial Intelligence in Medical Sciences Research Center, Smart University of Medical Sciences, Tehran, Iran.
Failure to predict stroke promptly may lead to delayed treatment, causing severe consequences like permanent neurological damage or death. Early detection using deep learning (DL) and machine learning (ML) models can enhance patient outcomes and mitigate the long-term effects of strokes. The aim of this study is to compare these models, exploring their efficacy in predicting stroke.
View Article and Find Full Text PDFAntenatal detection of pediatric surgical congenital abnormalities and its effect on maternal anxiety: a multicentre prospective study in a middle-income country.
Pediatr Surg Int
December 2024
Division of Paediatric & Neonatal Surgery, Department of Surgery, Faculty of Medicine, University of Malaya, 50603, Kuala Lumpur, Malaysia.
Background: In middle-income countries, healthcare systems face unique challenges in ensuring timely antenatal detection of congenital abnormalities that require pediatric surgical intervention. Early detection can significantly improve outcomes, yet resource constraints often limit access to diagnostic technologies. This study evaluates the antenatal detection rate of congenital abnormalities referred to pediatric surgical services in three Malaysian tertiary centers and examines its effect on maternal anxiety.
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