Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study

Non-invasive prediction nomogram for predicting significant fibrosis in patients with metabolic-associated fatty liver disease: a cross-sectional study

AbstractBackground and Aim This study aims to validate the efficacy of the conventional non-invasive score in predicting significant fibrosis in metabolic-associated fatty liver disease (MAFLD) and to develop a non-invasive prediction model for MAFLD.Methods This cross-sectional study was conducted...

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Bibliographic Details
Main Authors:	Fan Zhang, Yan Han, Yonghua Mao, Guojun Zheng, Longgen Liu, Wenjian Li
Format:	Article
Language:	English
Published:	Taylor & Francis Group 2024-12-01
Series:	Annals of Medicine
Subjects:	Metabolic-associated fatty liver disease liver fibrosis nomogram non-invasive prediction model diagnostics
Online Access:	https://www.tandfonline.com/doi/10.1080/07853890.2024.2337739

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