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10.1007/s42058-021-00056-4 http://scihub22266oqcxt.onion/10.1007/s42058-021-00056-4 33644690!7896877!33644690     free     free     free Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Chin+J+Acad+Radiol 2021 ; 4 (4): 241-247 Nephropedia Template TP {{tp|p=33644690|t=2021. Initial CT features of COVID-19 predicting clinical category |pdf=|usr=}}{{33644690}} gab.com Text Initial CT features of COVID-19 predicting clinical category JO: Chin J Acad Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33644690 #FOAvip PURPOSE: To analyze the initial CT features of different clinical categories of COVID-19. MATERIAL AND METHODS: A total of 86 patients with COVID-19 were analyzed, including the clinical, laboratory and imaging features. The following imaging features were analyzed, the lesion amount, location, density, lung nodule, halo sign, reversed-halo sign, distribution pattern, inner structures and changes of adjacent structures. Chi-square test, Fisher's exact test, or Mann-Whitney U test was used for the enumeration data. Binary logistic regression analysis was performed to draw a regression equation to estimate the likelihood of severe and critical category. The forward conditional method was employed for variable selection. RESULTS: Significant statistical differences were found in age (p = 0.001) and sex (p = 0.028) between mild and moderate and severe and critical category. No significant difference was found in clinical symptoms and WBC count between the two groups. The majority of cases (91.8%) showed multifocal lesions. The presence of GGO was higher in severe and critical category than in the mild and moderate category. (57.8% vs.31.7%, p = 0.015). Lymphocyte count was important indicator for the severe and critical category. CONCLUSION: The initial CT features of the different clinical category overlapped. Combining with laboratory test, especially the lymphocyte count, could help to predict the severity of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42058-021-00056-4. Twit Text FOAVip Initial CT features of COVID-19 predicting clinical category ????Chin J Acad Radiol http://www.kidney.de/pget.php?&tw=1&pmid=33644690 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33644690 #FOAvip English Wikipedia <ref>{{Cite pmid|33644690}}</ref> Initial CT features of COVID-19 predicting clinical category #MMPMID33644690 Fan L; Le W; Zou Q; Zhou X; Wang Y; Tang H; Han J; Liu S Chin J Acad Radiol 2021[]; 4 (4): 241-247 PMID33644690show ga PURPOSE: To analyze the initial CT features of different clinical categories of COVID-19. MATERIAL AND METHODS: A total of 86 patients with COVID-19 were analyzed, including the clinical, laboratory and imaging features. The following imaging features were analyzed, the lesion amount, location, density, lung nodule, halo sign, reversed-halo sign, distribution pattern, inner structures and changes of adjacent structures. Chi-square test, Fisher's exact test, or Mann-Whitney U test was used for the enumeration data. Binary logistic regression analysis was performed to draw a regression equation to estimate the likelihood of severe and critical category. The forward conditional method was employed for variable selection. RESULTS: Significant statistical differences were found in age (p = 0.001) and sex (p = 0.028) between mild and moderate and severe and critical category. No significant difference was found in clinical symptoms and WBC count between the two groups. The majority of cases (91.8%) showed multifocal lesions. The presence of GGO was higher in severe and critical category than in the mild and moderate category. (57.8% vs.31.7%, p = 0.015). Lymphocyte count was important indicator for the severe and critical category. CONCLUSION: The initial CT features of the different clinical category overlapped. Combining with laboratory test, especially the lymphocyte count, could help to predict the severity of COVID-19. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s42058-021-00056-4. äInitial CT features of COVID-19 predicting clinical category (epmc).pdf DeepDyve Pubget Overpricing