Nutrition
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Randomized Controlled Trial
Basic carbohydrate counting and glycemia in young people with type 1 diabetes in India: A randomized controlled trial.
The aim of this study was to evaluate the effect on glycemic control and acceptability of basic carbohydrate counting (BCC) in children and young adults with type 1 diabetes (T1DM). ⋯ Among children and young adults in our region with T1DM, BCC provided flexibility in food choices and perception of greater ease of insulin adjustment. Although BCC was equivalent to RNE in terms of glycemic control, larger studies may reveal benefit in outcomes in certain subgroups.
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Multicenter Study Observational Study
Discovery of distinct cancer cachexia phenotypes using an unsupervised machine-learning algorithm.
Cancer cachexia is a debilitating condition with widespread negative effects. The heterogeneity of clinical features within patients with cancer cachexia is unclear. The identification and prognostic analysis of diverse phenotypes of cancer cachexia may help develop individualized interventions to improve outcomes for vulnerable populations. The aim of this study was to show that the machine learning-based cancer cachexia classification model generalized well on the external validation cohort. ⋯ Machine learning is valuable for phenotype classifications of patients with cancer cachexia. Detection of clinically distinct clusters among cachexic patients assists in scheduling personalized treatment strategies and in patient selection for clinical trials.
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Observational Study
The Global Leadership Initiative on Malnutrition criteria for diagnosis of malnutrition and outcomes prediction in emergency abdominal surgery.
Malnutrition has adverse postoperative outcomes, especially in emergency surgery. Among the numerous tools for nutritional assessment, this study aims to investigate malnutrition diagnosed by Global Leadership Initiative on Malnutrition criteria and the Global Leadership Initiative on Malnutrition predictive value for outcomes after emergency abdominal surgery. ⋯ The Global Leadership Initiative on Malnutrition and Global Leadership Initiative on Malnutritison (muscle mass reduction excluded) had predictive value for adverse clinical outcomes due to malnutrition in patients undergoing emergency abdominal surgery.
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We investigate the accuracy and reliability of ChatGPT, an artificial intelligence model developed by OpenAI, in providing nutritional information for dietary planning and weight management. The results have a reasonable level of accuracy, with energy values having the highest level of conformity: 97% of the artificial intelligence values fall within a 40% difference from United States Department of Agriculture data. ⋯ These findings suggest that ChatGPT can provide reasonably accurate and consistent nutritional information. Further research is recommended to assess the model's performance across a broader range of foods and meals.
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