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10.1097/MD.0000000000022747 http://scihub22266oqcxt.onion/10.1097/MD.0000000000022747 33080737!7572001!33080737     free     free     free Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=33080737&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215       Medicine+(Baltimore) 2020 ; 99 (42): e22747 Nephropedia Template TP {{tp|p=33080737|t=2020. Differences and prediction of imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia: A multicenter study |pdf=|usr=}}{{33080737}} gab.com Text Differences and prediction of imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia: A multicenter study JO: Medicine (Baltimore) http://www.kidney.de/pget.php?&tw=1&pmid=33080737 #FOAvip To study the differences in imaging characteristics and prediction of COVID-19 and non-COVID-19 viral pneumonia through chest CT.Chest CT data of 128 cases of COVID-19 and 47 cases of non-COVID-19 viral pneumonia confirmed by several hospitals were retrospectively collected, the imaging performance was evaluated and recorded, different imaging features were statistically analyzed, and a prediction model and independent predicted imaging features were obtained by multivariable analysis.COVID-19 was more likely than non-COVID-19 pneumonia to have a high-grade ground glass opacities (P = .01), extensive lesion distribution (P < .001), mixed lesions of varying sizes (27.7% vs 57.0%, P = .001), subpleural prominence (23.4% vs 86.7%, P < .001), and lower lobe prominence (48.9% vs 82.0%, P < .001). However, peribronchial interstitial thickening was more likely to occur in non-COVID-19 viral pneumonia (36.2% vs 19.5%, P = .022). The statistically significant differences from multivariable analysis were the degree of ground glass opacities (P = .001), lesion distribution (P = .045), lesion size (P = .020), subpleural prominence (P < .001), and lower lobe prominence (P = .041). The sensitivity and specificity of the model were 94.5% and 76.6%, respectively, with an AUC of 0.91.The imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia are different, and the prediction model can further improve the specificity of chest CT diagnosis. Twit Text FOAVip Differences and prediction of imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia: A multicenter study ????Medicine (Baltimore) http://www.kidney.de/pget.php?&tw=1&pmid=33080737 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33080737 #FOAvip English Wikipedia <ref>{{Cite pmid|33080737}}</ref> Differences and prediction of imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia: A multicenter study #MMPMID33080737 Zhang B; Wang X; Tian X; Zhao X; Liu B; Wu X; Du Y; Huang G; Zhang Q Medicine (Baltimore) 2020[Oct]; 99 (42): e22747 PMID33080737show ga To study the differences in imaging characteristics and prediction of COVID-19 and non-COVID-19 viral pneumonia through chest CT.Chest CT data of 128 cases of COVID-19 and 47 cases of non-COVID-19 viral pneumonia confirmed by several hospitals were retrospectively collected, the imaging performance was evaluated and recorded, different imaging features were statistically analyzed, and a prediction model and independent predicted imaging features were obtained by multivariable analysis.COVID-19 was more likely than non-COVID-19 pneumonia to have a high-grade ground glass opacities (P = .01), extensive lesion distribution (P < .001), mixed lesions of varying sizes (27.7% vs 57.0%, P = .001), subpleural prominence (23.4% vs 86.7%, P < .001), and lower lobe prominence (48.9% vs 82.0%, P < .001). However, peribronchial interstitial thickening was more likely to occur in non-COVID-19 viral pneumonia (36.2% vs 19.5%, P = .022). The statistically significant differences from multivariable analysis were the degree of ground glass opacities (P = .001), lesion distribution (P = .045), lesion size (P = .020), subpleural prominence (P < .001), and lower lobe prominence (P = .041). The sensitivity and specificity of the model were 94.5% and 76.6%, respectively, with an AUC of 0.91.The imaging characteristics of COVID-19 and non-COVID-19 viral pneumonia are different, and the prediction model can further improve the specificity of chest CT diagnosis. |Betacoronavirus[MESH] |COVID-19[MESH] |Coronavirus Infections/*diagnostic imaging/*pathology[MESH] |Humans[MESH] |Lung/diagnostic imaging/pathology[MESH] |Pandemics[MESH] |Pneumonia, Viral/*diagnostic imaging/*pathology[MESH] |Retrospective Studies[MESH] |SARS-CoV-2[MESH]Differences and prediction of imaging characteristics of COVID-19 and (epmc).pdf DeepDyve Pubget Overpricing