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http://dx.doi.org/10.5694/j.1326-5377.2004.tb06073.x | DOI Listing |
Background: Coronary heart disease (CHD) and depression frequently co-occur, significantly impacting patient outcomes. However, comprehensive health status assessment tools for this complex population are lacking. This study aimed to develop and validate an explainable machine learning model to evaluate overall health status in patients with comorbid CHD and depression.
View Article and Find Full Text PDFAnnu Rev Clin Psychol
January 2025
3Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
Most people with mental health needs cannot access treatment; among those who do, many access services only once. Accordingly, single-session interventions (SSIs) may help bridge the treatment gap. We conducted the first umbrella review synthesizing research on SSIs for mental health problems and service engagement in youth and adults.
View Article and Find Full Text PDFJMIR Res Protoc
January 2025
Department of Pediatrics, School of Medicine, University of Virginia, Charlottesville, VA, United States.
Background: Low back pain (LBP) is highly prevalent and disabling, especially in agriculture sectors. However, there is a gap in LBP prevention and intervention studies in these physically demanding occupations, and to date, no studies have focused on horticulture workers. Given the challenges of implementing interventions for those working in small businesses, self-management offers an attractive and feasible option to address work-related risk factors and manage LBP.
View Article and Find Full Text PDFJ Med Internet Res
January 2025
Trinity College Dublin, Dublin, Ireland.
Background: Scientific implementation findings relevant to the implementation of internet-delivered cognitive behavioral therapy (iCBT) for depression and anxiety in adults remain sparse and scattered across different sources of published information. Identifying evidence-based factors that influence the implementation of iCBT is key to successfully using iCBT in real-world clinical settings.
Objective: This systematic review evaluated the following: (1) aspects that research articles postulate as important for the implementation of iCBT and (2) aspects relevant to the day-to-day running of iCBT services.
Interact J Med Res
January 2025
Medical Directorate, Lausanne University Hospital, Lausanne, Switzerland.
Large language models (LLMs) are artificial intelligence tools that have the prospect of profoundly changing how we practice all aspects of medicine. Considering the incredible potential of LLMs in medicine and the interest of many health care stakeholders for implementation into routine practice, it is therefore essential that clinicians be aware of the basic risks associated with the use of these models. Namely, a significant risk associated with the use of LLMs is their potential to create hallucinations.
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