{
"@context": "http://schema.org",
"@type": "ScholarlyArticle",
"@id": "https://doi.org/10.5281/zenodo.4025975",
"url": "https://zenodo.org/record/4025975",
"additionalType": "Journal article",
"name": "META-ANALYSIS STUDY TO DETERMINE DIAGNOSTIC PARAMETERS AS CHEST CT PREDICTIVE VALUES AND THE MAIN TRANSCRIPTASE POLYMERASE CHAIN",
"author": {
"name": "Farheen Farrukh Noor Ul Husnain",
"givenName": "Farheen Farrukh",
"familyName": "Noor Ul Husnain",
"@type": "Person"
},
"description": "Aim: Ongoing investigations have proposed that chest figured tomography checks could be utilized as an essential screening or symptomatic apparatus for COVID illness 2019 (Coronavirus) in pandemic areas. Purpose: To play out a meta-examination to assess symptomatic execution measures, counting prescient qualities, chest CT and Reverse Transcriptase-Polymerase chain reaction (RT-PCR). Materials and Methods: MEDLINE and Embase looked at COVID 19 concentrates for the results and additionally particularities of CT testing and RT-PCR testing at Mayo Hospital, Lahore from February 2020 to July 2020. The combined impact characteristics and consistency were analyzed by using unusual impact models. Results: The pool of results for Chest CT was 97% (96% CI: 92%), 97% (I4=96%), and 87% (96% CI: 80%, 97%, and I2=93%) of Chest CT. RT-PCR was 97%. For Chest CT filters, the Positive Predictive value increased from 2.7% to 35.8%, while the Negative Predictive value ranged from 95.4% to 99.8%. The PPV was between 49.7% and 99.8% for the RT-PCR, while the NPV was between 96.8% and 97.8%. CT was determined by the distribution of ailment duration, the number of comorbidity patients, and the number of asymptomatic patients (all p<0.05). The misfortune of RT-PCR with the magnitude of old PCR (p=0.02) was adversely associated. Conclusion: In comparison, the chest CT test for patients with suspected illness was not very normal in the region of COVID–19 (1–23.8%) (2.6–31.8% of the suspected illnesses). Keywords: Diagnostic Parameters, Transcriptase-Polymerase.",
"license": [
"https://creativecommons.org/licenses/by/4.0/legalcode",
"info:eu-repo/semantics/openAccess"
],
"datePublished": "2020-09-12",
"schemaVersion": "http://datacite.org/schema/kernel-4",
"publisher": {
"@type": "Organization",
"name": "Zenodo"
},
"provider": {
"@type": "Organization",
"name": "datacite"
}
}