Self-testing for covid-19

In a linked paper, Schuit and colleagues (doi:10.1136/bmj-2022-071215) tackle the issue of self-testing for covid-19. Despite a recent meta-analysis of the sensitivity and specificity of single sample testing, self-testing remains a vexing problem with many open questions. The authors attempt to answer two of the main ones: Does augmentation of nasal sampling with sampling from additional sites, such as the oropharynx, increase sensitivity, specificity, or both? And do these performance metrics change with the emergence of different viral types such as omicron subvariants?

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Publication Details

Journal
UNC Libraries
Published
2025-05-07
DOI
https://doi.org/10.17615/9z8y-rp71
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
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article

Self-testing for covid-19

Timothy Feeney, Charles Poole
UNC Libraries
COVID-19 diagnosis using AI
article

Self-testing for covid-19

Timothy Feeney, Charles Poole
article en

Abstract

In a linked paper, Schuit and colleagues (doi:10.1136/bmj-2022-071215) tackle the issue of self-testing for covid-19. Despite a recent meta-analysis of the sensitivity and specificity of single sample testing, self-testing remains a vexing problem with many open questions. The authors attempt to answer two of the main ones: Does augmentation of nasal sampling with sampling from additional sites, such as the oropharynx, increase sensitivity, specificity, or both? And do these performance metrics change with the emergence of different viral types such as omicron subvariants?

UNC Libraries
University of North Carolina at Chapel Hill (US)
Openalex Percentile: Top 60%
COVID-19 diagnosis using AI
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