Spss version 26

The results are reported as the mean ± standard deviation (SD). Statistical analyses between samples were performed using nonparametric tests followed Kruskal-Wallis tests (SPSS version 26, SPSS Inc. Armonk, NY, USA). A significant difference was set at p < 0.05. Correlation analysis was performed using SPSS version 26, SPSS Inc., Armonk, NY, USA. Principal component analysis (PCA) of bioactive components between sprouts and their antioxidant activities was performed using Python software version 3.10.5 (Python Software Foundation, Fredericksburg, VA, USA). The bioactive compounds were normalized before computing the PCA analysis. The PCA scores and loading variables were obtained from the PCA model. The PCA score plot was used to illustrate the correlation between bioactive compounds and its antioxidant. The loading plot indicated the correlation between the bioactive compounds and their antioxidant activity at 0.5 (moderate correlation) and 1.0 (strong correlation) as a black dashed circle and a red solid circle, respectively.

Data analysis was performed using IBM SPSS version 26 (IBM Corp., Armonk, New York, USA). We completed the Shapiro–Wilk test for continuous variables, and none of the continuous variables showed a Gaussian distribution. Continuous variables are presented as medians and interquartile ranges. Categorical variables are presented as frequencies (percentages). Continuous variables were compared using the Wilcoxon rank-sum test, and categorical variables were compared using Fisher's exact test. Continuous variables were compared using the Mann–Whitney U test, and categorical variables were compared using Fisher's exact test. Statistical significance was set at P < 0.05.
Univariate and multivariate logistic regression analyses were performed to assess risk factors for severe trauma. All significant variables in the univariate analysis were subjected to multivariate logistic regression analysis. All variables with a P value of less than 0.05 were included in a logistic regression analysis. To evaluate the performance of the multivariate logistic regression model, a receiver operating characteristic (ROC) curve was generated, and the area under the receiver operating characteristics curve (AUROC) was calculated. SPSS version 26.0 (SPSS Inc., Chicago, IL, USA) was used for the statistical analysis.

We used mean and SD for normally distributed variables, or median (interquartile) for the variables not normally distributed, to summarize the participants' demographics. Statistical analyses were performed using SPSS version 26.0. Comparisons were performed using the t-test or Mann–Whitney test for continuous variables and chi-square test for categorical variables, as appropriate. All tests were two-sided, and an α <0.05 was considered significant.
Propensity score matching (PSM) was performed using SPSS version 26.0 to ensure an even distribution of possible confounders between the two groups. A 1:1 matching range by using proximity matching was performed with a caliper width of 0.01. The underlying characteristics considered in the propensity-matching process were age, gender, hypertension, diabetes mellitus, hyperlipidemia, psychiatric disease, perinatal period, history of BP, and time from the onset to medical interventions. After matching patient characteristics, the Kaplan–Meier method was used to estimate survival curves. Cox proportional hazards models were used to estimate the hazard ratio (HR) of recovery and the corresponding 95% confidence interval (CI).

Descriptive analysis of patient characteristics included frequencies, means, and standard deviations. A Wilcoxon signed-rank test was used to test for significant changes in GoC levels after consultation, and all models were tested for proportionality using the test of parallel lines. Univariate ordinal logistic regressions were used to determine whether age, frailty stage, cognition, or COVID-19 status were associated with changes in care plans after consultation. All regression models were controlled for pre-intervention GoC level. All analyses were conducted using R version 4.1.0 and IBM SPSS version 26. (R Core Team, Vienna, Austria) and IBM SPSS version 26 (IBM Corporation, Armonk, NY, USA).
The Nova Scotia Health Research Ethics Board approved this research (REB# 26635).