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10.1002/mco2.14 http://scihub22266oqcxt.onion/10.1002/mco2.14 32838396!7436469!32838396     free     free     free Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 231.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 265.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 298.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       MedComm+(2020) 2020 ; 1 (2): 240-248 Nephropedia Template TP {{tp|p=32838396|t=2020. Radiomics-based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) and influenza A infected pneumonia |pdf=|usr=}}{{32838396}} gab.com Text Radiomics-based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) and influenza A infected pneumonia JO: MedComm (2020) http://www.kidney.de/pget.php?&tw=1&pmid=32838396 #FOAvip Clinicians have been faced with the challenge of differentiating between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) infected pneumonia (NCP) and influenza A infected pneumonia (IAP), a seasonal disease that coincided with the outbreak. We aim to develop a machine-learning algorithm based on radiomics to distinguish NCP from IAP by texture analysis based on computed tomography (CT) imaging. Forty-one NCP and 37 IAP patients admitted from January to February 6, 2019 admitted to two hospitals in Wenzhou, China. All patients had undergone chest CT examination and blood routine tests prior to receiving medical treatment. NCP was diagnosed by real-time RT-PCR assays. Eight of 56 radiomic features extracted by LIFEx were selected by least absolute shrinkage and selection operator regression to develop a radiomics score and subsequently constructed into a nomogram to predict NCP with area under the operating characteristics curve of 0.87 (95% confidence interval: 0.77-0.93). The nomogram also showed excellent calibration with Hosmer-Lemeshow test yielding a nonsignificant statistic (P = .904). The novel nomogram may efficiently distinguish between NCP and IAP patients. The nomogram may be incorporated to existing diagnostic algorithm to effectively stratify suspected patients for SARS-CoV-2 pneumonia. Twit Text FOAVip Radiomics-based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) and influenza A infected pneumonia ????MedComm (2020) http://www.kidney.de/pget.php?&tw=1&pmid=32838396 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32838396 #FOAvip English Wikipedia <ref>{{Cite pmid|32838396}}</ref> Radiomics-based model for accurately distinguishing between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) and influenza A infected pneumonia #MMPMID32838396 Zeng QQ; Zheng KI; Chen J; Jiang ZH; Tian T; Wang XB; Ma HL; Pan KH; Yang YJ; Chen YP; Zheng MH MedComm (2020) 2020[Sep]; 1 (2): 240-248 PMID32838396show ga Clinicians have been faced with the challenge of differentiating between severe acute respiratory syndrome associated coronavirus 2 (SARS-CoV-2) infected pneumonia (NCP) and influenza A infected pneumonia (IAP), a seasonal disease that coincided with the outbreak. We aim to develop a machine-learning algorithm based on radiomics to distinguish NCP from IAP by texture analysis based on computed tomography (CT) imaging. Forty-one NCP and 37 IAP patients admitted from January to February 6, 2019 admitted to two hospitals in Wenzhou, China. All patients had undergone chest CT examination and blood routine tests prior to receiving medical treatment. NCP was diagnosed by real-time RT-PCR assays. Eight of 56 radiomic features extracted by LIFEx were selected by least absolute shrinkage and selection operator regression to develop a radiomics score and subsequently constructed into a nomogram to predict NCP with area under the operating characteristics curve of 0.87 (95% confidence interval: 0.77-0.93). The nomogram also showed excellent calibration with Hosmer-Lemeshow test yielding a nonsignificant statistic (P = .904). The novel nomogram may efficiently distinguish between NCP and IAP patients. The nomogram may be incorporated to existing diagnostic algorithm to effectively stratify suspected patients for SARS-CoV-2 pneumonia. äRadiomics-based model for accurately distinguishing between severe acu(epmc).pdf DeepDyve Pubget Overpricing