Spss version 12.0 for windows

All data were expressed as mean ± standard deviation (SD). The differences in SP-IR levels in animals submitted to dry needling and sham operation were calculated. One-way analysis of variance (ANOVA) was employed to determine the differences in SP-IR levels among groups. Post hoc comparisons between groups were examined using Scheffe's method. A P value of <.05 was considered statistically significant. All data were analyzed using SPSS version 12.0 for Windows (SPSS Inc., IL, USA).

All data are expressed as mean ± standard deviation or number with percentage. Student t test was used to compare continuous variables and χ² test or Fisher exact test for categorical variables. The logistic regression model was used for univariate and multivariate analysis of risk factors for PD morbidity. All statistical analyses were carried out using SPSS version 12.0 for Windows (SPSS, Chicago, IL), and P < 0.05 were considered statistically significant.

Data were analyzed using SPSS version 12.0 for Windows. Descriptive statistics were used to describe the general characteristics and levels of patient satisfaction of the sample. t-tests were used to examine the associations of patient satisfaction with general characteristics including sex, residential region, religion, employment status, revisit of outpatient clinic, type of doctor, and use of other tertiary hospitals. ANOVA was used to examine the associations of patient satisfaction with age, religion, and total length of being an outpatient of the institution. Scheffé's test was used as a post hoc test to determine differences among subgroups based on demographic characteristics with respect to patient satisfaction. Simultaneous multiple regression analyses were performed to examine service factors and individual variables predicting patient satisfaction.

Statistical analysis was performed using a software package (SPSS, version 12.0 for Windows; SPSS, Chicago, III.). Fisher’s exact test was used for the comparisons of two binary variables, and Student’s t test was used for comparisons of quantitative variables. The ultrasound features were compared with the histological diagnosis results to determine the sensitivity, specificity, negative predictive value, and positive predictive value. A p value less than 0.05 was considered to indicate statistical significance. A receiver operating characteristic curve (ROC) was also generated, and the area under the curve (AUC) was calculated to determine the diagnostic performance of the quantitative EI. In addition, multiple logistic regression analysis with significant variables in the univariate logistic regression model was performed to determine independent US predictors for malignancy from the US characteristics that showed statistical significance.
Inter-observer agreement was assessed for US characteristics using the Cohen kappa statistic. The interpretation of kappa values: 0.00–0.20 indicated slight agreement; 0.21–0.40, fair agreement; 0.41–0.60, moderate agreement; 0.61–0.80, substantial agreement; and 0.80–1.00, almost perfect agreement15 (link)22 (link).

The data were expressed as their mean ± standard deviations (SD) and analyzed by SPSS (Version 12.0 for Windows, SPSS Inc., Chicago, IL, USA). The significant differences were determined at p < 0.05 by one-way analysis of variance (ANOVA) and Tukey–Kramer’s multiple comparison post-hoc test. The correlations among the variables were determined by the two-tailed Pearson correlation analysis (p < 0.01). PCA was performed using Origin software (version 2019, Microcal Inc., Northampton, MA, USA).