Methods of planned clinical experiments on dietotherapy, adapted for the study of food additives in the absence of an adequate placebo, were improved due to the modification of the mathematical analysis. The schedule of the experiment involves exclusion of nonuniformity, reduction of the number of necessary studies and high accuracy of the final results. For the statistical analysis of the data obtained in the experiment a program has been proposed for the microcomputer.
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Minimising harms of tight glycaemic control in older patients with type 2 diabetes.
Afr J Prim Health Care Fam Med
December 2024
Department of Anaesthesiology, Pharmacology and Therapeutics, Faculty of Medicine, University of British Columbia, Vancouver.
In older adults with type 2 diabetes (T2DM), tight glycaemic control (HbA1c 7%) can result in more harm than benefit, especially when using insulin or sulfonylureas. Older adults are at higher risk for adverse drug events, especially hypoglycaemia, which may cause falls, confusion and hospitalisations. This Therapeutic Letter evaluates the risks of tight glycaemic control in older adults with T2DM, focusing on deprescribing diabetes medications in those over 65, especially those with multimorbidity and polypharmacy.
View Article and Find Full Text PDFMachine learning approaches for risk prediction after percutaneous coronary intervention: a systematic review and meta-analysis.
Eur Heart J Digit Health
January 2025
Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114, USA.
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View Article and Find Full Text PDFMachine learning applications in placenta accreta spectrum disorders.
Eur J Obstet Gynecol Reprod Biol X
March 2025
Mother and Newborn Health Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran.
This review examines the emerging applications of machine learning (ML) and radiomics in the diagnosis and prediction of placenta accreta spectrum (PAS) disorders, addressing a significant challenge in obstetric care. It highlights recent advancements in ML algorithms and radiomic techniques that utilize medical imaging modalities like magnetic resonance imaging (MRI) and ultrasound for effective classification and risk stratification of PAS. The review discusses the efficacy of various deep learning models, such as nnU-Net and DenseNet-PAS, which have demonstrated superior performance over traditional diagnostic methods through high AUC scores.
View Article and Find Full Text PDFConstruction of a poor prognosis prediction and visualization system for intracranial aneurysm endovascular intervention treatment based on an improved machine learning model.
Front Neurol
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Department of Neurology, The Third People's Hospital of Yibin, Yibin, China.
Objective: To evaluate the clinical utility of improved machine learning models in predicting poor prognosis following endovascular intervention for intracranial aneurysms and to develop a corresponding visualization system.
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Front Med (Lausanne)
January 2025
Department of Hand and Microsurgery, Peking University Shenzhen Hospital, Shenzhen, China.
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