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10.21037/apm-20-2603 http://scihub22266oqcxt.onion/10.21037/apm-20-2603 33725765!ä!33725765 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 229.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 263.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Ann+Palliat+Med 2021 ; 10 (2): 2048-2061 Nephropedia Template TP {{tp|p=33725765|t=2021. Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19 |pdf=|usr=}}{{33725765}} gab.com Text Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19 JO: Ann Palliat Med http://www.kidney.de/pget.php?&tw=1&pmid=33725765 #FOAvip BACKGROUND: The outbreak of COVID-19 poses a major and urgent threat to global public health. CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. This study aimed to retrospectively analyze abnormal lung changes following initial computed tomography (CT) among patients with coronavirus disease 2019 (COVID-19) in Yunnan, and to evaluate the effectiveness of a chest CT-based model for the diagnosis of COVID-19. METHODS: One hundred and nine patients with COVID-19 pneumonia confirmed with the positive new coronavirus nucleic acid antibody who exhibited abnormal findings on initial CT were retrospectively analyzed. Thereafter, changes in the number, distribution, shape, and density of the lesions were observed. Further, the epidemiological, clinical, and CT imaging findings (+/-) were correlated. Following univariate and multivariate logistic regression analysis, receiver operating characteristic (ROC) curves were generated for significant factors, and models were established to evaluate the diagnostic ability of CT for COVID-19. RESULTS: Our results showed significant differences between patients with COVID-19 in epidemiological history (first, second, and third generation), clinical type (moderate, severe, and critical), and abnormal CT imaging characteristics (+/-) (P<0.05). Moreover, significant differences in abnormal CT imaging characteristics, including region, extent, and focus, were observed between the first generation and the other generations (P<0.05). For the diagnosis of COVID-19, the areas under the ROC curves for logistic regression models 1, 2, and 3 were 0.8016 (95% CI: 0.6759-0.9274), 0.9132 (95% CI: 0.8571-0.9693), and 0.9758 (95% CI: 0.9466-1), respectively. CONCLUSIONS: The ROC curve regression model based on chest CT signs displayed a high diagnostic value for COVID-19. Twit Text FOAVip Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19 ????Ann Palliat Med http://www.kidney.de/pget.php?&tw=1&pmid=33725765 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33725765 #FOAvip English Wikipedia <ref>{{Cite pmid|33725765}}</ref> Clinical utility of a computed tomography-based receiver operating characteristic curve model for the diagnosis of COVID-19 #MMPMID33725765 Li X; Yuan F; Zhang Z; Yang J; Zhang J; Li Z; Peng Y; Jiang Y; Yi W; Ma J; Zhao W; He B; Wu L; Wang K Ann Palliat Med 2021[Feb]; 10 (2): 2048-2061 PMID33725765show ga BACKGROUND: The outbreak of COVID-19 poses a major and urgent threat to global public health. CT findings associated with COVID-19 pneumonia from initial diagnosis until patient recovery. This study aimed to retrospectively analyze abnormal lung changes following initial computed tomography (CT) among patients with coronavirus disease 2019 (COVID-19) in Yunnan, and to evaluate the effectiveness of a chest CT-based model for the diagnosis of COVID-19. METHODS: One hundred and nine patients with COVID-19 pneumonia confirmed with the positive new coronavirus nucleic acid antibody who exhibited abnormal findings on initial CT were retrospectively analyzed. Thereafter, changes in the number, distribution, shape, and density of the lesions were observed. Further, the epidemiological, clinical, and CT imaging findings (+/-) were correlated. Following univariate and multivariate logistic regression analysis, receiver operating characteristic (ROC) curves were generated for significant factors, and models were established to evaluate the diagnostic ability of CT for COVID-19. RESULTS: Our results showed significant differences between patients with COVID-19 in epidemiological history (first, second, and third generation), clinical type (moderate, severe, and critical), and abnormal CT imaging characteristics (+/-) (P<0.05). Moreover, significant differences in abnormal CT imaging characteristics, including region, extent, and focus, were observed between the first generation and the other generations (P<0.05). For the diagnosis of COVID-19, the areas under the ROC curves for logistic regression models 1, 2, and 3 were 0.8016 (95% CI: 0.6759-0.9274), 0.9132 (95% CI: 0.8571-0.9693), and 0.9758 (95% CI: 0.9466-1), respectively. CONCLUSIONS: The ROC curve regression model based on chest CT signs displayed a high diagnostic value for COVID-19. |*ROC Curve[MESH] |*Tomography, X-Ray Computed[MESH] |COVID-19/*diagnostic imaging[MESH] |China[MESH] |Humans[MESH] |Logistic Models[MESH]Clinical utility of a computed tomography-based receiver operating cha(epmc).pdf DeepDyve Pubget Overpricing