Spss 26.0 statistical

The agreement of the 2 post-processors image quality and interobserver were determined by calculation of kappa (K) values. The strength of agreement was rated as follows: 0= poor, 0–0.20= slight, 0.21–0.40= fair, 0.41–0.60= moderate, 0.61–0.80= substantial, and 0.81–1.00= almost perfect (11 (link)). Sensitivity, specificity, accuracy, and positive predictive value (PPV) and negative predictive value (NPV) of mCTA for differential diagnosis of ID were calculated using the consensus DSA evaluation as the reference standard. The net reclassification index (NRI) (12 (link)) was used to evaluate the differential efficacy of mCTA and sCTA. Mean and standard deviation (SD) were used to describe quantitative variables, whereas in the case of qualitative variables, percentages were used. First, a bivariate analysis was carried out, using Student’s t-test for independent samples or Mann-Whitney U-test for quantitative variables, whereas the chi-square test was used for qualitative variables. Wilcoxon matched-pairs signed-rank test and Fisher’s exact probability test were used to examine the ability of different images to distinguish between ID and IA. A P value <0.05 was considered statistically significant. All statistical analyses were performed using the SPSS 26.0 statistical software package (IBM Corp, Armonk, NY, USA).

Excel 2016 for Windows (Microsoft Corporation, Seattle, WA, USA) and SPSS 26.0
statistical software for Windows (IBM Corp., Armonk, NY, USA) were used for the
statistical analysis. Categorical variables are expressed as frequency.
Continuous variables are expressed as mean ± standard deviation. Student’s t
test and the chi-square test were used. A P value of <0.05 was considered
statistically significant.

The data were analyzed and processed using SPSS 26.0 statistical software (IBM Corp.). The main evaluation indicators of the STFT colon polyp detection model is accuracy, precision, recall, and F1-score (also known as balanced F score). The performance of the STFT detection model in image and video testing is evaluated by calculating the receiver operating characteristic curve (ROC) and the area under the ROC curve (AUC) to obtain the best threshold. Apart from AUC, other indicators are calculated using true positive (TP), true negative (TN), false positive (FP), and false negative (FN) (refer to supplementary materials for formulas). The comparison between the STFT polyp detection model and endoscopists is conducted using the chi-square test, and accuracy, sensitivity, specificity, positive predictive value, and negative predictive value are calculated. P value < 0.05 indicates a statistically significant difference.

The SPSS 26.0 statistical software package (IBM SPSS Statistics, IBM Corporation, Armonk, NY, USA) was used for statistical analysis of data. Continuous variables were expressed as mean ± standard deviation or median (interquartile range [IQR]), and classified data were expressed as a percentage. Pearson’s chi-square test or Fisher's exact test was used for univariate comparisons of differences between two groups. Variables with P < 0.05 in univariate analysis were introduced into the final multivariate model. Multivariate logistic regression was applied for multivariate analysis based on forward stepwise logistic regression. Odds ratio (OR) values and 95% confidence intervals (95%CI) were used to describe the independent factors associated with Gram-negative and CRO infections. When P < 0.05 in the two-tailed test, the difference was considered to be statistically significant.