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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.
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.
As a journalist, you can create a free Muck Rack account to customize your profile, list your contact preferences, and upload a portfolio of your best work.
