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Objective @#To investigate the diagnostic efficacy of the nomogram model based on Chinese thyroid ima ging reporting and data system (C TIRADS) combined with shear wave elastography ( SWE) and clinically inde pendent risk factors for category IV thyroid nodules .@*Methods @#2D-ultrasound images and SWE images of 256 pa tients (269 nodules ) with category IV thyroid nodules were analyzed . The sensitivity , specificity , and accuracy of the diagnosis by C-TIRADS and SWE were calculated using pathological findings as the gold standard . Receiver op erating characteristic (ROC) curves were plotted , and the area under the curve (AUC) was obtained . Independent risk factors for thyroid nodules were screened by univariate and multifactorial logistic regression analyses , a risk model was developed and a nomogram model was plotted , and a calibration curve analysis was used to assess the accuracy of prediction . ROC of the nomogram model was plotted , and the diagnostic efficacy of C-TIRADS , SWE and nomogram model based on independent risk factors was compared according to the AUC in category IV thyroid nodules . @*Results @#The sensitivity , specificity , and accuracy of C-TIRADS for differentiating malignant and benign nodules was 0.921 , 0.724 and 0.844 respectively , the AUC was equal to 0.822 with a 95% confidence interval (95% CI) of 0.775 - 0.870 . The sensitivity , specificity and accuracy of SWE were 0.701 , 0.981 , 0.814 respec tively , and the AUC was 0.833 (95% CI: 0.795 - 0.872) . Multifactorial logistic regression analysis suggested that C-TIRADS classification , mean value of elasticity (E-mean ) age and aspect ratio were independent risk factors for identifying benign and malignant thyroid nodules . The sensitivity , specificity and accuracy of the nomogram model established based on the above four factors were 0.957 , 0.943 and 0.959 , the AUC was 0.963 (95% CI: 0.943 - 0.984) , which showed a diagnostic efficacy superior to that of C-TIRADS or SWE alone .@*Conclusion@#The nomogram model , constructed based on C-TIRADS , SWE and clinically independent risk factors , can improve the efficacy in diagnosing category IV thyroid nodules , with a better clinical application value .
ABSTRACT
Objective:To evaluate and rank the effects of different non-pharmaceutical interventions on the management of blood glucose in children and adolescents with type 1 diabetes by Bayesian network Meta-analysis.Methods:Computer search for PubMed, Web of Science, Cochrane Library, Embase, CINAHL, China Biomedical Literature Database, China Knowledge Network, Wangfang Data and VIP Database, China and the United States Clinical Registration Center, the search time limit is from the establishment of each database to December 2020. Use WinBUGS, GeMTC, Stata software for blood glucose management data analysis.Results:Finally, 37 articles were included, with a total of 4 188 cases, involving 14 non-pharmaceutical intervention programs. The results of the network Meta-analysis showed that when different non-pharmacological interventions were used, the effectiveness of blood glucose management in children and adolescents with type 1 diabetes was ranked as follows: peer support education first, cognitive behavioral therapy second, advanced diabetes education third, family support intervention fourth, self guided intervention (GSD-Y) fifth, case training sixth, positive psychological intervention seventh, mobile phone based remote intervention eighth, diabetes education ninth, supportive consultation tenth, network based remote intervention eleventh position, multi-system therapy multi system therapy twelfth, conventional nursing thirteenth, motivational interview fourteenth.Conclusions:The available evidence shows that peer support education and cognitive behavioral therapy show certain advantages in blood glucose management in children and adolescents with type 1 diabetes, while motivational interviews perform the worst. Future research will further consider other potential benefits of non-pharmacological interventions, such as emotional health and diabetes self-management behavior.