Raw performance metrics for the Logistic Regression (LR) model.

The table presents the raw output from the bootstrap analysis, showing the mean, lower, and upper confidence interval bounds for the AUC of local and federated models, and the performance gain (ΔAUC). For each of the 21 participating hospitals, the table lists the mean Area Under the Curve (AUC) and its 95% confidence interval (CI) for both the locally trained and the federated models. The final columns quantify the performance gain via the mean Delta AUC (ΔAUC) and its 95% CI, providing a direct, hospital-by-hospital comparison between the two learning approaches for this linear model. (XLSX)本表格展示了自举分析（bootstrap analysis）的原始输出结果，呈现了局部模型与联邦模型的曲线下面积（Area Under the Curve, AUC）的均值、置信区间下界与上界，以及性能增益值（ΔAUC）。针对参与本次研究的21家医院，表格逐一列出了局部训练模型与联邦模型的平均AUC及其95%置信区间（confidence interval, CI）。末尾列项通过平均ΔAUC及其95%置信区间量化了性能提升幅度，可针对该线性模型实现两种学习范式间逐医院的直接对比。（XLSX）