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10.1093/aje/kwab093 http://scihub22266oqcxt.onion/10.1093/aje/kwab093 33823529!8083455!33823529     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 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 217.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 251.2 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 284.79999999999995 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Am+J+Epidemiol 2021 ; 190 (8): 1689-1695 Nephropedia Template TP {{tp|p=33823529|t=2021. Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain Reaction and Lateral Flow Immunoassay Tests With Bayesian Latent-Class Models |pdf=|usr=}}{{33823529}} gab.com Text Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain Reaction and Lateral Flow Immunoassay Tests With Bayesian Latent-Class Models JO: Am J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=33823529 #FOAvip Our objective was to estimate the diagnostic accuracy of real-time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for coronavirus disease 2019 (COVID-19), depending on the time after symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent-class models, which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (immunoglobulin G (IgG) and/or immunoglobulin M (IgM)) assays using RT-PCR as the reference method. The sensitivity of RT-PCR was 0.68 (95% probability interval (PrI): 0.63, 0.73). IgG/M sensitivity was 0.32 (95% PrI :0.23; 0.41) for the first week and increased steadily. It was 0.75 (95% PrI: 0.67; 0.83) and 0.93 (95% PrI: 0.88; 0.97) for the second and third weeks after symptom onset, respectively. Both tests had a high to absolute specificity, with higher point median estimates for RT-PCR specificity and narrower probability intervals. The specificity of RT-PCR was 0.99 (95% PrI: 0.98; 1.00). and the specificity of IgG/IgM was 0.97 (95% PrI: 0.92, 1.00), 0.98 (95% PrI: 0.95, 1.00) and 0.98 (95% PrI: 0.94, 1.00) for the first, second, and third weeks after symptom onset. The diagnostic accuracy of LFIA varies with time after symptom onset. Bayesian latent-class models provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles. Twit Text FOAVip Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain Reaction and Lateral Flow Immunoassay Tests With Bayesian Latent-Class Models ????Am J Epidemiol http://www.kidney.de/pget.php?&tw=1&pmid=33823529 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=33823529 #FOAvip English Wikipedia <ref>{{Cite pmid|33823529}}</ref> Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain Reaction and Lateral Flow Immunoassay Tests With Bayesian Latent-Class Models #MMPMID33823529 Kostoulas P; Eusebi P; Hartnack S Am J Epidemiol 2021[Aug]; 190 (8): 1689-1695 PMID33823529show ga Our objective was to estimate the diagnostic accuracy of real-time polymerase chain reaction (RT-PCR) and lateral flow immunoassay (LFIA) tests for coronavirus disease 2019 (COVID-19), depending on the time after symptom onset. Based on the cross-classified results of RT-PCR and LFIA, we used Bayesian latent-class models, which do not require a gold standard for the evaluation of diagnostics. Data were extracted from studies that evaluated LFIA (immunoglobulin G (IgG) and/or immunoglobulin M (IgM)) assays using RT-PCR as the reference method. The sensitivity of RT-PCR was 0.68 (95% probability interval (PrI): 0.63, 0.73). IgG/M sensitivity was 0.32 (95% PrI :0.23; 0.41) for the first week and increased steadily. It was 0.75 (95% PrI: 0.67; 0.83) and 0.93 (95% PrI: 0.88; 0.97) for the second and third weeks after symptom onset, respectively. Both tests had a high to absolute specificity, with higher point median estimates for RT-PCR specificity and narrower probability intervals. The specificity of RT-PCR was 0.99 (95% PrI: 0.98; 1.00). and the specificity of IgG/IgM was 0.97 (95% PrI: 0.92, 1.00), 0.98 (95% PrI: 0.95, 1.00) and 0.98 (95% PrI: 0.94, 1.00) for the first, second, and third weeks after symptom onset. The diagnostic accuracy of LFIA varies with time after symptom onset. Bayesian latent-class models provide a valid and efficient alternative for evaluating the rapidly evolving diagnostics for COVID-19, under various clinical settings and different risk profiles. |Antibodies, Viral/blood[MESH] |Bayes Theorem[MESH] |COVID-19 Nucleic Acid Testing/*statistics & numerical data[MESH] |COVID-19 Serological Testing/*statistics & numerical data[MESH] |COVID-19/*diagnosis/immunology[MESH] |Humans[MESH] |Immunoassay/*statistics & numerical data[MESH] |Latent Class Analysis[MESH] |Real-Time Polymerase Chain Reaction/*statistics & numerical data[MESH] |SARS-CoV-2/genetics/immunology[MESH] |Sensitivity and Specificity[MESH]Diagnostic Accuracy Estimates for COVID-19 Real-Time Polymerase Chain (epmc).pdf DeepDyve Pubget Overpricing