CGM Methods Data Evaluation

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.本数据集收录自动态血糖监测（continuous glucose monitoring, CGM）文献的结构化提取数据，旨在评估可重复性研究所需的方法学报告完整性。经研究者共识界定，本数据集设定了七项核心方法学报告特征，用以表征可重复开展CGM数据制备与分析所需的核心要素：CGM品牌与型号、CGM数据平台、数据提取流程、数据清洗/标准化/转换方法、缺失值处理方案、分析的CGM指标以及分析方法。上述特征均已直接收录于本数据集。本数据集的数据由六名经过培训的评审人员，于2024年8月至2025年8月的12个月周期内，从符合纳入标准的文献中手动提取得到。本数据集可用于分析CGM领域研究的报告规范情况，并助力评估与可重复性相关的方法学透明度。