Nutrition
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To assess the concurrent and predictive validity of different combinations of Global Leadership Initiative on Malnutrition (GLIM) criteria in patients with colorectal cancer considering different indicators of reduced muscle mass (MM) and the effects of the disease. ⋯ Satisfactory concurrent validity was not verified. The GLIM diagnosis of malnutrition was associated with postoperative complications and mortality.
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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.