Use my Search Websuite to scan PubMed, PMCentral, Journal Hosts and Journal Archives, FullText. Kick-your-searchterm to multiple Engines kick-your-query now !>

A dictionary by aggregated review articles of nephrology, medicine and the life sciences Your one-stop-run pathway from word to the immediate pdf of peer-reviewed on-topic knowledge.

10.1111/tmi.13476 http://scihub22266oqcxt.onion/10.1111/tmi.13476 32790891!7436218!32790891     free     free     free Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 213.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Trop+Med+Int+Health 2020 ; 25 (11): 1385-1394 Nephropedia Template TP {{tp|p=32790891|t=2020. A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil |pdf=|usr=}}{{32790891}} gab.com Text A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil JO: Trop Med Int Health http://www.kidney.de/pget.php?&tw=1&pmid=32790891 #FOAvip OBJECTIVE: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system. METHODS: We analysed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who travelled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation. RESULTS: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41-96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51-97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%). CONCLUSION: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modelling epidemiological trends. Twit Text FOAVip A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil ????Trop Med Int Health http://www.kidney.de/pget.php?&tw=1&pmid=32790891 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=32790891 #FOAvip English Wikipedia <ref>{{Cite pmid|32790891}}</ref> A model to predict SARS-CoV-2 infection based on the first three-month surveillance data in Brazil #MMPMID32790891 Diaz-Quijano FA; da Silva JMN; Ganem F; Oliveira S; Vesga-Varela AL; Croda J Trop Med Int Health 2020[Nov]; 25 (11): 1385-1394 PMID32790891show ga OBJECTIVE: COVID-19 diagnosis is a critical problem, mainly due to the lack or delay in the test results. We aimed to obtain a model to predict SARS-CoV-2 infection in suspected patients reported to the Brazilian surveillance system. METHODS: We analysed suspected patients reported to the National Surveillance System that corresponded to the following case definition: patients with respiratory symptoms and fever, who travelled to regions with local or community transmission or who had close contact with a suspected or confirmed case. Based on variables routinely collected, we obtained a multiple model using logistic regression. The area under the receiver operating characteristic curve (AUC) and accuracy indicators were used for validation. RESULTS: We described 1468 COVID-19 cases (confirmed by RT-PCR) and 4271 patients with other illnesses. With a data subset including 80% of patients from Sao Paulo (SP) and Rio Janeiro (RJ), we obtained a function which reached an AUC of 95.54% (95% CI: 94.41-96.67%) for the diagnosis of COVID-19 and accuracy of 90.1% (sensitivity 87.62% and specificity 92.02%). In a validation dataset including the other 20% of patients from SP and RJ, this model exhibited an AUC of 95.01% (92.51-97.5%) and accuracy of 89.47% (sensitivity 87.32% and specificity 91.36%). CONCLUSION: We obtained a model suitable for the clinical diagnosis of COVID-19 based on routinely collected surveillance data. Applications of this tool include early identification for specific treatment and isolation, rational use of laboratory tests, and input for modelling epidemiological trends. |*Models, Biological[MESH] |*Population Surveillance[MESH] |Adult[MESH] |Area Under Curve[MESH] |Brazil[MESH] |COVID-19/*diagnosis[MESH] |Coronavirus Infections[MESH] |Female[MESH] |Humans[MESH] |Male[MESH] |Middle Aged[MESH] |Pandemics[MESH] |ROC Curve[MESH] |Reproducibility of Results[MESH] |SARS-CoV-2[MESH]A model to predict SARS-CoV-2 infection based on the first three-month(epmc).pdf DeepDyve Pubget Overpricing