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http://dx.doi.org/10.1097/JHM-D-21-00176 | DOI Listing |
JMIR Res Protoc
January 2025
Data and Web Science Group, School of Business Informatics and Mathematics, University of Manneim, Mannheim, Germany.
Background: The rapid evolution of large language models (LLMs), such as Bidirectional Encoder Representations from Transformers (BERT; Google) and GPT (OpenAI), has introduced significant advancements in natural language processing. These models are increasingly integrated into various applications, including mental health support. However, the credibility of LLMs in providing reliable and explainable mental health information and support remains underexplored.
View Article and Find Full Text PDFAnnu Rev Biomed Data Sci
January 2025
1Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, California, USA;
Cancer remains a leading cause of death globally. The complexity and diversity of cancer-related datasets across different specialties pose challenges in refining precision medicine for oncology. Foundation models offer a promising solution.
View Article and Find Full Text PDFCien Saude Colet
January 2025
Universidade Federal do Paraná. R. XV de Novembro 1299, Centro. 80060-000 Curitiba PR Brasil.
PLoS One
January 2025
Alliance for Research in Exercise Nutrition and Activity (ARENA), Allied Health and Human Performance, University of South Australia, Adelaide, Australia.
Background: Cold-water immersion (CWI) has gained popularity as a health and wellbeing intervention among the general population.
Objective: This systematic review and meta-analysis aimed to evaluate the psychological, cognitive, and physiological effects of CWI in healthy adults.
Methods: Electronic databases were searched for randomized trials involving healthy adults aged ≥ 18 years undergoing acute or long-term CWI exposure via cold shower, ice bath, or plunge with water temperature ≤15°C for at least 30 seconds.
J Eval Clin Pract
February 2025
School of Primary and Allied Health Care, Monash University, Melbourne, Australia.
Background: Clinical practice guidelines (CPGs) are moving toward greater consideration of population-level differences, like health inequities, when creating management recommendations. CPGs have the potential to reduce or perpetuate health inequities. The intrinsic design factors of electronic interfaces that contain CPGs are known barriers to guideline use.
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