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10.1038/s41598-021-90991-0 http://scihub22266oqcxt.onion/10.1038/s41598-021-90991-0 34078950!8172890!34078950     free     free     free Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 215.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Sci+Rep 2021 ; 11 (1): 11591 Nephropedia Template TP {{tp|p=34078950|t=2021. Study on the prognosis predictive model of COVID-19 patients based on CT radiomics |pdf=|usr=}}{{34078950}} gab.com Text Study on the prognosis predictive model of COVID-19 patients based on CT radiomics JO: Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34078950 #FOAvip Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong's test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19. Twit Text FOAVip Study on the prognosis predictive model of COVID-19 patients based on CT radiomics ????Sci Rep http://www.kidney.de/pget.php?&tw=1&pmid=34078950 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34078950 #FOAvip English Wikipedia <ref>{{Cite pmid|34078950}}</ref> Study on the prognosis predictive model of COVID-19 patients based on CT radiomics #MMPMID34078950 Wang D; Huang C; Bao S; Fan T; Sun Z; Wang Y; Jiang H; Wang S Sci Rep 2021[Jun]; 11 (1): 11591 PMID34078950show ga Making timely assessments of disease progression in patients with COVID-19 could help offer the best personalized treatment. The purpose of this study was to explore an effective model to predict the outcome of patients with COVID-19. We retrospectively included 188 patients (124 in the training set and 64 in the test set) diagnosed with COVID-19. Patients were divided into aggravation and improvement groups according to the disease progression. Three kinds of models were established, including the radiomics, clinical, and combined model. Receiver operating characteristic curves, decision curves, and Delong's test were used to evaluate and compare the models. Our analysis showed that all the established prediction models had good predictive performance in predicting the progress and outcome of COVID-19. |*Tomography, X-Ray Computed[MESH] |Aged[MESH] |COVID-19/*diagnostic imaging/etiology[MESH] |Diagnosis, Computer-Assisted/methods[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Models, Theoretical[MESH] |Prognosis[MESH]Study on the prognosis predictive model of COVID-19 patients based on (epmc).pdf DeepDyve Pubget Overpricing