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Articles by Yangming Li
Comparison of three malnutrition assessment criteria for predicting postoperative complications and overall survival in colorectal cancer
Abstract Background: Malnutrition is highly prevalent in patients with colorectal cancer and is associated with adverse postoperative outcomes and shortened survival. However, the comparative predictive performance of commonly used nutritional assessment tools—PG-SGA, ESPEN, and GLIM criteria—remains unclear. Methods: A total of 1,258 patients who underwent radical resection for colorectal cancer were retrospectively enrolled.
Dynamic weight loss trajectories and machine learning-based prediction of malnutrition in colorectal cancer patients after curative surgery
Abstract Objective: This study aimed to identify distinct postoperative weight loss trajectories and develop a machine learning-based model for predicting malnutrition risk at 6 months after curative resection for colorectal cancer (CRC). Methods: This prospective observational study enrolled 205 CRC patients who underwent curative resection.
Sexual function recovery trajectories and predictive modeling in men undergoing rectal cancer surgery
Abstract Sexual dysfunction is a common and distressing sequela of rectal cancer surgery. However, the longitudinal patterns of its recovery and reliable early predictive factors are not well established, which hinders proactive management strategies. This prospective observational cohort study enrolled 154 male patients undergoing curative-intent surgery for rectal cancer.
cp-normality and coprime action in finite groups
Advanced search Communications in Algebra Latest Articles Submit an article Journal homepage Full Article Figures & data References Citations Metrics Reprints & Permissions Read this article /doi/full/10.1080/00927872.2025.2594162?needAccess=true Abstract Let G and A be finite groups of coprime orders, with A acting on G via automorphisms.
Under‐reporting of COVID‐19 in the Northern Health Authority region of British Columbia
Abstract en Asymptomatic and pauci-symptomatic presentations of COVID-19 along with restrictive testing protocols result in undetected COVID-19 cases. Estimating undetected cases is crucial to understanding the true severity of the outbreak. We introduce a new hierarchical disease dynamics model based on the N-mixtures hidden population framework. The new models make use of three sets of disease count data per region: reported cases, recoveries and deaths.
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