Spss statistical software version 25

Patient demographics and clinical characteristics (age, sex, risk factor prevalence, and ABI) between the IC, CLI, and control participants were compared using chi-square and Fisher exact tests for categorical variables and analysis of variance (ANOVA) for continuous variables. A one-way analysis of covariance (ANCOVA) was used to test differences in BH4 and BH2 concentrations as well as the ratio of BH4/BH2 between the IC, CLI, and control groups, controlling for significant covariates. A Pearson correlation was also calculated to test the association between ABI and BH4, BH2, and the BH4/BH2 ratio.
A one-way ANCOVA was also used to test differences in arginine, citrulline, ADMA, SDMA, the arginine/ADMA ratio, the arginine/SDMA ratio, NOx, protein carbonyls, and 4-HNE between groups. A Pearson correlation was further calculated to test the association between the metabolites and ratios, NOx, protein carbonylation, 8-OHdG, and 4-HNE with ABI, as well as BH4, BH2, and the BH4/BH2 ratio. An independent sample t-test was used to test the difference in AGEs between diabetic and non-diabetic patients. A Pearson correlation was also calculated to test the association between AGEs and ABI as well as NOx. All analyses were performed using SPSS statistical software version 25 (IBM, Armonk, NY, USA). Significance was set at α < 0.05.

IBM SPSS Statistical Software version 25 (IBM, Armonk, NY, USA) was used to test the statistical correlations between the cellular metabolic activity and CFU/mL (of S. aureus and P. aeruginosa) and the previously mentioned surface properties. Since the data obtained were largely nonparametric, Spearman’s rho correlations were used.

Data presentation is principally descriptive and no a priori hypotheses were tested. Continuous data are presented as mean values with standard deviation (SD) and categorical values as frequency counts and percentages. Missing data were not replaced. Variables were compared between men and women and across OSA severity grades using the χ² test for categorical variables and the Wilcoxon test for continuous values. Two-sided tests were used in all cases and a probability threshold of 0.05 was considered significant. Variables associated with severe OSA, hypertension and diabetes, as dependent variables were investigated using multivariate logistic regression analysis. In a first step, all documented variables were evaluated independently in a univariate analysis. Those variables for which a significant association (p <0.05) with the dependent variable was observed were then entered into a multivariate analysis and odds ratios generated. Age, sex and BMI were included in all the multivariate analyses. All data analysis was performed using IBM, SPSS statistical software Version 25.

First, frequencies and percentages with 95% confidence intervals (CI) for experiencing psychosocial hazards and covariates were calculated according to level of PE (low/high).
Second, the crude and adjusted prevalence ratios (PR and aPR) of experiencing psychosocial hazards according to levels of PE were explored by means of generalized linear models, with the Poisson family and robust variances, with the low PE group as reference [27 (link)]. The choice of Poisson models was based on the high prevalence of the outcomes studied (>10%) and thereby to avoid an overestimation of the effect. Adjustment was performed in two steps: first by adjusting for gender and age and thereafter including country of birth and education (fully adjusted model).
Third, gender-specific prevalence of experiencing psychosocial hazards was calculated according to level of PE. Due to the small sample size, PR and aPR stratified on gender were deemed infeasible.
Analyses were conducted using SPSS statistical software version 25 (IBM Corp, Armonk, NY, USA) and STATA 16.0 (Stata corporation, College Station, TX, USA).

The mean ± standard error of the mean (SEM) values were calculated for descriptive statistics. The numbers and percentages of cases were computed for categorical variables. To check the normality of the data, we used the Kolgomorov-Smirnov test and/or visual inspection of the Q–Q plots. In the case of non-normal distributed variables, e.g., bilirubin, GGT, AST, ALT, amylase and lipase, we transformed our data into the logarithmic scale to achieve normal distribution; then we applied parametric tests (graphs were prepared from the raw data without transformation). To identify significant differences between groups, we used the following statistical tests for the whole dataset and subgroups as well.
To observe differences between two groups, the independent t-test was applied (Figs. 1A, 3E, 6), to compare more than two groups; we used one-way ANOVA with a Tukey post hoc test to adjust the results for alpha error (Figs. 3A–D, 4). The association between categorical variables was examined with the Chi-square test and the Fisher's exact test, depending on the sample size (Figs. 1, 3A,B,D). All statistical tests were performed using SPSS statistical software version 25 (IBM Corporation, Armonk, NY).

A retrospective, cross-sectional study was performed in the ED of a tertiary medical center in Israel from January 2014 to March 2020. A primary search of the healthcare database was conducted using the keywords “electric scooter” or/and “electric bike” and/or “powered scooter” or/and “powered bike” and “injury/injured”. Of the 1417 patients identified, 1234 were actually involved in an E-bike or P-scooter accident and had sufficient available data for inclusion in the study. The following parameters were collected from the medical files: demographics (age, gender), type of two-wheel electric vehicle used, hospitalization (yes/no), length of hospitalization (if relevant), use of imaging, type of imaging (if relevant), surgery (yes/no), duration of surgery (if relevant), and status at the end of the ED visit. Findings were compared between patients who required hospitalization and those who did not. The study protocol was approved by the Helsinki Committee of Rabin Medical Center (approval number 0194-20-RMC).
The data were analyzed with SPSS statistical software, version 25 (IBM^®, Armonk, NY, USA). Continuous variables were summarized by mean and standard deviation, and discrete variables by frequency. Univariate analysis was performed using chi-square (χ2) test, and independent samples were analyzed with Mann–Whitney test. Significance was set at a p-value lower than 5%.

Scores were tabulated and displayed using a heat map to compare each country’s achievement against various indicators. To examine correlation between financial variables and attainment of indicators, we used Gross Domestic Product per capita (GDP per capita) and OOP costs and assessed correlation with overall aggregate scores, NCD medicine scores and NCD technologies. Spearman’s rank correlation coefficient (rho) was calculated and the size of the correlation coefficient was interpreted based on the classification stated by Mukaka, where a value approaching 1 indicates high correlation, and a value less than 0.3 suggests low or negligible correlation [31 (link)]. Data analysis was done using IBM SPSS statistical software, version 25.