Explorative modelling.

To model the effect of the entropy on certain clinical parameters / endpoints, explorative modelling with logistic regression for categorical clinical endpoints (QMG-drop, MMS) and linear regression for clinical endpoints with linear scaling (QMG-score, MG-duration, pre- and postoperative prednisone load) were performed. Respectively, one clinical parameter is thereby modeled on basis of four entropy values (for the number of follicles in the CD23-staining, the follicle grading in the CD23-staining, the grading of the intratyhmic fat and the grading of the atrophy). The table shows the p-value (* indicates significance at the 5%-level) per variable in the adjusted model that contains all the other variables against the respective clinical endpoint.为探究熵（entropy）对特定临床参数与临床终点的影响，本研究针对分类临床终点（QMG-drop、MMS）采用逻辑回归（logistic regression）建模，针对具备线性标度（linear scaling）的临床终点（QMG-score、MG-duration、术前及术后泼尼松负荷量）采用线性回归（linear regression）建模，以此开展探索性分析。针对每一项临床参数，均基于四个熵值构建对应模型，这四个熵值分别对应CD23染色（CD23-staining）中的滤泡计数、CD23染色中的滤泡分级、胸腺内脂肪分级以及萎缩分级。该表格展示了校正模型（adjusted model）中各变量的P值（p-value），其中*代表该变量在5%显著性水平下具有统计学意义，该校正模型纳入其余所有变量，并针对各自对应的临床终点开展关联分析。