Economic analysis of interventions to reduce non-communicable diseases can encourage countries to increase investment, say
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| http://dx.doi.org/10.1136/bmj.l1648 | DOI Listing |
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Department of Respiratory Diseases, The First Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou, Henan, China.
Background: Patient-reported outcomes (PROs) have become indispensable measures in allergic rhinitis (AR) clinical trials. However, there is currently no scale specifically designed for the Chinese population. This study aimed to develop and validate the patient-reported outcome scale for allergic rhinitis (AR-PRO) to provide a reliable tool for AR patients in China.
View Article and Find Full Text PDF[Water Availability and Security in the Development of Non-Communicable Chronic Diseases: New Risk Factors?].
Rev Med Chil
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Unidad de Investigación en Educación, Universidad Católica del Maule, Talca, Chile.
Global Burden of Gallbladder Cancer Attributable to Modifiable Risk Factors.
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January 2025
Department of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Gallbladder cancer (GBC) is a highly lethal and often overlooked malignancy increasingly affecting young adults. This study quantified the global proportion of GBC cases attributable to 10 key modifiable risk factors, employing Monte Carlo simulations and estimates from field-wide systematic review and meta-analysis. Approximately three-quarters of global GBC cases are attributable to key modifiable factors (74.
View Article and Find Full Text PDFImpact of Donor CMV-Seropostive Grafts on Prognosis of Liver Transplantation for Hepatocellular Carcinoma.
J Med Virol
January 2025
Department of Hepatobiliary and Pancreatic Surgery, Hangzhou First People's Hospital, Hangzhou, China.
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Noncommunicable Diseases Research Center, Fasa University of Medical Sciences, Fasa, Iran.
Introduction: In Iran, the assessment of osteoporosis through tools like dual-energy X-ray absorptiometry poses significant challenges due to their high costs and limited availability, particularly in small cities and rural areas. Our objective was to employ a variety of machine learning (ML) techniques to evaluate the accuracy and precision of each method, with the aim of identifying the most accurate pattern for diagnosing the osteoporosis risks.
Methods: We analysed the data related to osteoporosis risk factors obtained from the Fasa Adults Cohort Study in eight ML methods, including logistic regression (LR), baseline LR, decision tree classifiers (DT), support vector classifiers (SVC), random forest classifiers (RF), linear discriminant analysis (LDA), K nearest neighbour classifiers (KNN) and extreme gradient boosting (XGB).
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