CGM Methods Data Evaluation

Published: 23 March 2026| Version 1 | DOI: 10.17632/yv75yvj2fz.1
Contributors:
Ashley DuNova, Casey Rand, Mandy Shao, Agatha Scheideman, Cindy Ho, Alessandra Ayers, Kevin Nguyen, Eric Williams, Shahid Shah, David Klonoff, Juan Espinoza

Description

This dataset contains structured extraction data from continuous glucose monitoring (CGM) literature, designed to evaluate the completeness of methodological reporting required for reproducibility. Seven core methodological reporting features were defined through investigator consensus to represent essential components for reproducing CGM data preparation and analysis: CGM make and model, CGM data platform, data extraction processes, data cleaning/normalization/transformation methods, approach to missingness, CGM metrics analyzed, and analytic methods. These features are directly captured within the dataset. Data were manually extracted from eligible articles by six trained reviewers over a 12-month period (August 2024–August 2025). The dataset supports analysis of reporting practices in CGM research and enables assessment of reproducibility-related methodological transparency.

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Categories

Diabetes, Continuous Glucose Monitor

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