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10.1097/RCT.0000000000001100 http://scihub22266oqcxt.onion/10.1097/RCT.0000000000001100 32889972!ä!32889972 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       J+Comput+Assist+Tomogr 2020 ; 44 (5): 627-632 Nephropedia Template TP {{tp|p=32889972|t=2020. Predictive Computed Tomography and Clinical Features for Diagnosis of COVID-19 Pneumonia: Compared With Common Viral Pneumonia |pdf=|usr=}}{{32889972}} gab.com Text Predictive Computed Tomography and Clinical Features for Diagnosis of COVID-19 Pneumonia: Compared With Common Viral Pneumonia JO: J Comput Assist Tomogr http://www.kidney.de/pget.php?&tw=1&pmid=32889972 #FOAvip OBJECTIVE: To determine the predictive computed tomography (CT) and clinical features for diagnosis of COVID-19 pneumonia. METHODS: The CT and clinical data including were analyzed using univariate analysis and multinomial logistic regression, followed by receiver operating characteristic curve analysis. RESULTS: The factors including size of ground grass opacity (GGO), GGO with reticular and/or interlobular septal thickening, vascular enlargement, "tree-in-bud" opacity, centrilobular nodules, and stuffy or runny nose were associated with the 2 groups of viral pneumonia, as determined by univariate analysis (P < 0.05). Only GGO with reticular and/or interlobular septal thickening, centrilobular nodules, and stuffy or runny nose remained independent risk factors in multinomial logistic regression analysis. Receiver operating characteristic curve analysis showed that the area under curve of the obtained logistic regression model was 0.893. CONCLUSION: Computed tomography and clinical features including GGO with reticular and/or interlobular septal thickening, absence of centrilobular nodules, and absence of stuffy or runny nose are potential patients with COVID-19 pneumonia. Twit Text FOAVip Predictive Computed Tomography and Clinical Features for Diagnosis of COVID-19 Pneumonia: Compared With Common Viral Pneumonia ????J Comput Assist Tomogr http://www.kidney.de/pget.php?&tw=1&pmid=32889972 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32889972 #FOAvip English Wikipedia <ref>{{Cite pmid|32889972}}</ref> Predictive Computed Tomography and Clinical Features for Diagnosis of COVID-19 Pneumonia: Compared With Common Viral Pneumonia #MMPMID32889972 Zhou C; Luo L; Luo Z; Shen X J Comput Assist Tomogr 2020[Sep]; 44 (5): 627-632 PMID32889972show ga OBJECTIVE: To determine the predictive computed tomography (CT) and clinical features for diagnosis of COVID-19 pneumonia. METHODS: The CT and clinical data including were analyzed using univariate analysis and multinomial logistic regression, followed by receiver operating characteristic curve analysis. RESULTS: The factors including size of ground grass opacity (GGO), GGO with reticular and/or interlobular septal thickening, vascular enlargement, "tree-in-bud" opacity, centrilobular nodules, and stuffy or runny nose were associated with the 2 groups of viral pneumonia, as determined by univariate analysis (P < 0.05). Only GGO with reticular and/or interlobular septal thickening, centrilobular nodules, and stuffy or runny nose remained independent risk factors in multinomial logistic regression analysis. Receiver operating characteristic curve analysis showed that the area under curve of the obtained logistic regression model was 0.893. CONCLUSION: Computed tomography and clinical features including GGO with reticular and/or interlobular septal thickening, absence of centrilobular nodules, and absence of stuffy or runny nose are potential patients with COVID-19 pneumonia. |*Betacoronavirus[MESH] |Adult[MESH] |COVID-19[MESH] |COVID-19 Testing[MESH] |Clinical Laboratory Techniques/*methods[MESH] |Coronavirus Infections/*diagnosis[MESH] |Diagnosis, Differential[MESH] |Female[MESH] |Humans[MESH] |Lung/*diagnostic imaging[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnosis[MESH] |Predictive Value of Tests[MESH] |Reproducibility of Results[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]Predictive Computed Tomography and Clinical Features for Diagnosis of (epmc).pdf DeepDyve Pubget Overpricing