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Article summary:
1. A multilevel clustering approach was developed to classify patients with type 2 diabetes according to their characteristics, using continuous glucose monitoring (CGM) data.
2. The method extracted knowledge-based and statistics-based features from CGM data and used Fisher score and variables cluster analysis to fuse features into low dimensions.
3. Four subgroups of patients with type 2 diabetes were identified, each with distinct statistical features and clinical phenotypes.
Article analysis:
The article is generally reliable and trustworthy in its reporting of the research conducted on the use of a multilevel clustering approach for classifying patients with type 2 diabetes according to their characteristics, using continuous glucose monitoring (CGM) data. The authors provide a detailed description of the methods used in the study, as well as a thorough discussion of the results obtained from the analysis. The article does not appear to be biased or one-sided in its reporting, nor does it contain any promotional content or partiality towards any particular point of view. All possible risks associated with the use of CGM are noted in the article, and both sides of the argument are presented equally. Furthermore, all claims made by the authors are supported by evidence from the study, and no missing points of consideration or unexplored counterarguments are present in the text.