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Low dietary diversity and associated factors among adult people with HIV patients attending ART clinics of Ethiopia. Systematic review and meta-analysis.
AIDS Res Ther
January 2025
Department of Nursing, College of Medicine and Health Science, Debre Berhan University, Debre Berhan, Ethiopia.
Introduction: Dietary diversity is the utilization of food and food groups consumed by individuals over 24 h, which is an indicator of a diet's micronutrient adequacy. Dietary management in people with HIV patients is the key to sustaining their day-to-day activities and contributing to their lively hood. The level of dietary diversity among HIV-positive patients in Ethiopia shows considerable variation, ranging from 29 to 71.
View Article and Find Full Text PDFModeling the determinants of cigarette consumption in Iranian households with children under 5 years of age using the Income and Expenditure Survey 2021.
Arch Public Health
January 2025
Department of Biostatistics & Epidemiology, School of Public Health, Infectious Ophthalmologic Research Center, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran.
Background: Cigarette smoking remains a significant public health concern, with detrimental effects on both smokers and those exposed to secondhand smoke. This study investigates the factors influencing smoking behaviors in Iranian households, focusing on households with children under five years old.
Methods: We conducted a cross-sectional analysis of 8751 Iranian households using data from the Iranian Household Income and Expenditure Survey (HIES) collected by the Statistical Center of Iran (SCI) in 2021.
Development and validation of an EHR-based risk prediction model for geriatric patients undergoing urgent and emergency surgery.
BMC Anesthesiol
January 2025
Kaiser Permanente Division of Research, 2000 Broadway, Oakland, CA, 94612, USA.
Background: Clinical determination of patients at high risk of poor surgical outcomes is complex and may be supported by clinical tools to summarize the patient's own personalized electronic health record (EHR) history and vitals data through predictive risk models. Since prior models were not readily available for EHR-integration, our objective was to develop and validate a risk stratification tool, named the Assessment of Geriatric Emergency Surgery (AGES) score, predicting risk of 30-day major postoperative complications in geriatric patients under consideration for urgent and emergency surgery using pre-surgical existing electronic health record (EHR) data.
Methods: Patients 65-years and older undergoing urgent or emergency non-cardiac surgery within 21 hospitals 2017-2021 were used to develop the model (randomly split: 80% training, 20% test).
Association of long-term weight management pharmacotherapy with multiple health outcomes: an umbrella review and evidence map.
Int J Obes (Lond)
January 2025
Center for Optometry, Gansu Provincial Hospital, Lanzhou, China.
Background: Multiple meta-analyses (MAs) have demonstrated that six pharmacotherapies, including orlistat, liraglutide, phentermine/topiramate, naltrexone/bupropion, semaglutide, and tirzepatide, improve weight loss and weight maintenance. However, few studies have synthesized and evaluated the quality of this evidence.
Objective: To identify the relevant MAs of randomized clinical trials (RCTs) that explored the association between the six pharmacotherapies and obesity-related health outcomes and adverse events (AEs).
Classifying Unstructured Text in Electronic Health Records for Mental Health Prediction Models: Large Language Model Evaluation Study.
JMIR Med Inform
January 2025
School of Social Policy & Practice, University of Pennsylvania, Philadelphia, PA, United States.
Background: Prediction models have demonstrated a range of applications across medicine, including using electronic health record (EHR) data to identify hospital readmission and mortality risk. Large language models (LLMs) can transform unstructured EHR text into structured features, which can then be integrated into statistical prediction models, ensuring that the results are both clinically meaningful and interpretable.
Objective: This study aims to compare the classification decisions made by clinical experts with those generated by a state-of-the-art LLM, using terms extracted from a large EHR data set of individuals with mental health disorders seen in emergency departments (EDs).
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