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9 849 protocols using spss version 22

1

Statistical Analysis of Acute Flare in CHB

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SPSS version 22.0 (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. The normally distributed data and nonnormally distributed continuous data were presented as the mean ± SD, median (interquartile range), respectively. The significance of differences between different groups was performed by the t test or Mann–Whitney test. The correlation between virological, biochemical, and serological parameters and acute flare of CHB was conducted by logistic regression analysis. SPSS version 22.0 was used to calculate the area under the receiver operating characteristic curve (AUROC) with 95% confidence interval (CI). Single asterisk (*) represents p value <0.05 and double asterisks (**) represent p value <0.01.
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2

Assessing CPG Reporting and Methodological Quality

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We used Excel 2016 software (Microsoft) to document the rates and percentages of reporting of the RIGHT items, as well as the score for the domains of the AGREE II. The means and ranges of the CPG scores for each AGREE II domain were calculated. Agreement among reviewers was measured by intraclass correlation coefficient (ICC) calculated by SPSS version 22.0, according to previous studies (17 (link)-20 (link)). The ICC level was classified as poor (<0.40), fair (0.40–0.59), good (0.60–0.74), or excellent (0.75–1.00). The relationship between the methodological quality and the reporting quality of included CPGs were analyzed by Spearman’s correlation using SPSS version 22.0 software, according to previous research (22 (link)). We made a judgment of “fully reported”, “partially reported”, “unreported”, or “not applicable”, with corresponding scores of 1, 0.5, 0, and 0, respectively. The total scores that could be obtained with the RIGHT checklist and the AGREE II tool were 35 and 161, respectively. The relationship between the methodological quality and the reporting quality is presented in the scatter gram.
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3

Analysis of Partograph Knowledge and Usage

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Collected data were checked for completeness and consistency, coded and entered into Epi Info Version 3.5.1 software. Data from Epi Info Version 3.5.1 software were exported into SPSS version 22.0 statistical software for analysis. Data were explored and cleaned prior to analysis using SPSS version 22.0. Descriptive statistic was done to compute frequencies, percentages, mean, standard deviation and median of independent and dependent variables accordingly. Bivariate and multivariate logistic regression analysis were carried out to examine the relationship between independent variables and the health professionals’ knowledge and use of the partograph. Statistically significant association was declared considering adjusted odds ratio at 95% confidence interval and p value less than 0.05.
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4

Profiling College Students' Sports Motivation

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Latent profile analysis (LPA) is a statistical method distinguishing between types of subjects, further examining the relationship between types and other variables (Liu et al., 2020 ). Using this method to research college students’ sports behavior motivation makes it possible to cluster but descend the multiple sports behavior motivations of college students and analyze the variability of different sports behavior motivation types.
We first used the SPSS version 22.0 statistical analysis software to conduct a descriptive statistical analysis of sample characteristics. This was followed by Mplus version 7.0 to conduct a LPA of college students’ sports behavior motivation. Finally, the SPSS version 22.0 statistical analysis software was used to explore the relationship between college students’ sports behavior motivation types and family social class using the chi-square analysis and multiple logistic regression analysis relationships.
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5

AI-Powered LARC Identification via CT

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Data were analyzed from 1 May 2022 to 31 May 2023, using Cohen’s kappa statistics (SPSS version 22.0) for CT and MRI inter-rater reliability, chi-square tests, and ANOVA for patient characteristic comparisons. Survival curves were evaluated using the Kaplan–Meier method and log-rank tests, employing IBM SPSS version 22.0.
This methodology ensured a robust and comprehensive approach to evaluating AI’s capability in accurately identifying LARC cases through CT imaging, potentially transforming diagnostic processes in oncological care.
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6

Predicting Blood Transfusion in THA Patients

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Concerning clinical data with missing values, we employed the expectation maximization method in SPSS Version 22.0 for imputation. The original dataset and the data with imputed missing values are available in the supplementary materials. Clinical data were presented as Mean ± SD and Median [P25, P75]. We performed statistical analyses using SPSS Version 22.0, employing the Mann–Whitney U test, Student's t-test, or chi-square test as appropriate. These tests were employed to compare disparities between patients with and without blood transfusions (BT and Non-BT), depending on the data type. The significance level was set at α = 0.05. To establish a predictive model for blood transfusion in THA patients, we employed the logistic regression algorithm and created a nomogram to visualize the prediction model. The 'corrplot' package in R software was used to generate correlation heat maps illustrating the correlation between clinical data in the prediction model. The accuracy of the prediction model was determined by the ROC curve and calibration curve analyses (The 'pROC' package in R software).
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7

Statistical Analysis of Interobserver Agreement

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Statistical analysis was performed using SPSS version 22.0. Descriptive statistics were given as counts and percentages for categorical variables; interquartile ranges and median were given for nonparametric continuous variables; standard deviation and mean for parametric continuous variables. Chi-square test was performed for categorical data. Kruskal–Wallis test was used to compare three groups on continuous variables. A P-value of less than 0.05 was considered statistically significant. Kappa statistics were calculated using SPSS version 22.0 to determine the proportion of interobserver agreement beyond that expected by chance. The method for estimating an overall kappa value in cases of multiple observers and categories is based on the work of Landis and Koch. A value of k.1.0 corresponds to complete agreement; 0, no agreement; and less than 0, disagreement. Landis and Koch suggested that a kappa value 0.20 indicates slight agreement; 0.21–0.40, fair agreement; 0.41–0.60, moderate agreement; 0.61–0.80, substantial agreement; and 0.81–1.00, almost perfect agreement (14 (link)).
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8

Predictors of Ibutilide Rhythm Conversion

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The data was collected using a structured questionnaire. Data entry and analysis was done on SPSS version 22.0. Categorical variables were expressed as proportion and percentages. Quantitative variables were expressed in mean ± standard deviation and median (interquartile range). Univariate analysis in form of Chi-square test for categorical variable and T test for quantitative variables was done to explore the association of factors with rhythm conversion. To compare median across the groups, Mann-Whitny U test was conducted. Statistical significance was defined at a p value of less than 0.05. Binary logistic regression using Forward Conditional method was applied by including factors from univariate analysis (with p-value <0.20). Independent factors like serum magnesium level at time of Ibutilide injection, serum potassium level at the time of injection, beta blocker usage, and left atrial size were included in the model. ROC curve was plotted using SPSS version 22.0 between serum magnesium level at time of admission, serum magnesium level at time of ibutilide injection and conversion of arrhythmia. The area under curve and cut-off point of magnesium at maximum sensitivity and specificity was determined.
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9

Spermine Effects on In Vivo and In Vitro Experiments

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Statistical analysis for all data was performed using SPSS version 22.0 (SPSS Inc., Chicago, IL, USA). Shapiro-Wilk's W-test and Levene's test were employed to determine the normality and homogeneity, respectively. The data were presented as mean ± standard deviation. For in vivo experiment, 2 (spermine; 0 or 0.4 mmol/kg BW) × 4 (treatment time; 7 h or 3, 6, 7 d) factorial experimental data were subjected to a two-way analysis of variance (ANOVA) by the general linear model procedure of SPSS version 22.0. The main effects in this study included spermine level, treatment time and their interaction. If at least one main effect or interaction was statistically significant, the differences were identified by Duncan's multiple range tests among the treatments at the level of P < 0.05. Pearson's correlation coefficients among the data were achieved by the correlation analysis of SPSS.
For in vitro experiment, the data were subjected to ANOVA, followed by Duncan's multiple range tests to determine the significant differences among the treatments at the level of P < 0.05.
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10

Assessing Pesticide Exposure using AChE Inhibition

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The data was entered in forms created with Epi info version 7 and extracted and analyzed using both Epi info version 7 and IBM SPSS Version 22. The prevalence of AChE inhibition was expressed in the form of percentages. Mean or median (wherever applicable) were used to quantify levels of AChE inhibition results stratified by key variables related to exposure characteristics. Univariable analyses were used to examine the unadjusted association of various independent variables with elevated inhibition levels.
An independent samples t-test was conducted to compare difference in AChE inhibition between the exposed and the control group. Similarly, difference in AChE levels between different variables were compared using an independent samples t-test analysis. A one-way ANOVA was conducted to test the difference in AChE levels between locations, age groups, farming experience in years. When a significant difference was detected by the ANOVA, treatment means were compared using Tukey’s HSD test. Differences in treatment means were considered statistically significant at the p = 0.05 level. The data were analyzed using IBM SPSS Version 22 and results are presented as means ± SD.
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