We developed a computer-based tailored risk presentation and risk communication aid for colorectal cancer prevention. To evaluate the effectiveness of the tool, we randomized 353 participants to receive different risk presentation formats (relative plus absolute risk vs. absolute risk only vs. none) and different levels of engagement with the risk communication (active vs. passive). Findings suggest the tool to be useful for correcting misperceptions about personal risk. Of those with inaccurate risk perception at baseline, more than half of the participants in intervention groups had corrected risk perceptions at post-test, compared to only 12% in the control group.
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http://dx.doi.org/10.1080/10810730490425295 | DOI Listing |
Am J Kidney Dis
January 2025
Renal Division, Department of Internal Medicine, National Taiwan University Hospital, Taipei, Taiwan; National Taiwan University Hospital Study Group of ARF (NSARF), Taipei, Taiwan.
Rationale & Objective: Glucagon-like peptide-1 receptor agonists (GLP-1RAs) improve cardiac and kidney outcomes in patients with diabetes; however their efficacy in individuals with reduced estimated glomerular filtration rate (eGFR) is uncertain. This study evaluated the effects of GLP-1RAs on kidney and cardiovascular (CV) outcomes in patients with chronic kidney disease (CKD).
Study Design: Systematic review and meta-analysis of randomized controlled trials (RCTs) reported through May 25, 2024.
Life Sci
January 2025
Department of Biotechnology, College of Biomedical & Health Science, Konkuk University, Chungju, Republic of Korea; Research Institute for Biomedical & Health Science (RIBHS), Konkuk University, Chungju, Republic of Korea. Electronic address:
Many patients with liver diseases are exposed to the risk of hepatic encephalopathy (HE). The incidence of HE in liver patients is high, showing various symptoms ranging from mild symptoms to coma. Liver transplantation is one of the ways to overcome HE.
View Article and Find Full Text PDFIntensive Crit Care Nurs
January 2025
Faculty of Medicine and Health, The University of Sydney Susan Wakil School of Nursing and Midwifery Camperdown NSW Australia; Western Sydney Local Health District, North Parramatta, NSW 2141, Australia. Electronic address:
Background: Emergency departments have high levels of uncertainty, long wait times, resource shortages, overcrowding and a constantly changing environment. Patient experience and patient safety are directly linked, yet levels of patient experience are stagnant. To improve emergency nursing care and patient experience, an emergency nursing framework HIRAID® (History including Infection risk, Red flags, Assessment, Interventions, Diagnostics, communication, and reassessment) was implemented in 29 Australian emergency departments.
View Article and Find Full Text PDFNutrients
January 2025
Department of Nutrition, General Hospital of Thessaloniki "G. Gennimatas", 41 Ethnikis Aminis Str., GR-54635 Thessaloniki, Greece.
Background/objectives: Feeding and eating disorders (FEDs) constitute an important mental health problem today, especially among youngsters. The Sick, Control, One, Fat, Food (SCOFF) questionnaire was developed 25 years ago and remains the most frequently applied screening tool for FEDs among adults and youngsters. The aim of the present study was to translate and adapt the SCOFF questionnaire to the Greek language, using a tertiary-setting adolescent sample.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Communication and Information Engineering, Xi'an University of Science and Technology, Xi'an 710054, China.
Artificial intelligence (AI), particularly through advanced large language model (LLM) technologies, is reshaping coal mine safety assessment methods with its powerful cognitive capabilities. Given the dynamic, multi-source, and heterogeneous characteristics of data in typical mining scenarios, traditional manual assessment methods are limited in their information processing capacity and cost-effectiveness. This study addresses these challenges by proposing an embodied intelligent system for mine safety assessment based on multi-level large language models (LLMs) for multi-source sensor data.
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