Statistical analysis
hematoma, NRI
REGRESSION, HEMATOMA
Continuous variables, such as age and total muscle strength on the worse side, were evaluated in terms of mean ± standard deviation (SD) or median and interquartile range (IQR) as appropriate, while categorical variables were expressed as counts and percentages. Univariable analysis of candidate variables was performed using univariate logistic regression. Variables with a two-sided P-value < 0.2 were entered into the multivariate logistic regression to build the prediction model using the forward stepwise method. The multivariable analysis results were presented using a Forest plot with the ‘forest’ package. P-value < 0.05 was considered statistically significant in multivariate logistic regression and the GiViTI calibration test. Univariable analysis and multivariable logistic regression were performed using IBM SPSS Statistics 25.0 software (IBM Corporation, NY, USA). The AUROC, GiViTI calibration belt, DCA analyses, NRI and IDI were performed using R version 4.2.1 (R Project for Statistical Computing) with the pROC, givitiR, rmda, and PredictABEL libraries (During statistical analysis, missing data of hematoma volume (22/1333,1.7%) and blood glucose (3/1333,0.23%) were considered missing completely at random (MCAR) [