Evidence map›Paper›PMID 33636148›Full record

ReviewThe Lancet. Infectious diseases2021

Scaling up COVID-19 rapid antigen tests: promises and challenges.

Rosanna W Peeling, Piero L Olliaro, Debrah I Boeras, Noah Fongwen

Abstract readReview
In one paragraph

Review in The Lancet. Infectious diseases, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 198 papers, 5 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
198citing papers in PubMed, 5 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

198 citing papers in PubMed, 5 syntheses or guidelines pooled it.

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  5. Real-world clinical performance of commercial SARS-CoV-2 rapid antigen tests in suspected COVID-19: A systematic meta-analysis of available data as of November 20, 2020.International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases · 2021
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138 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Rosanna W PeelingDepartment of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK. Electronic address: rosanna.peeling@lshtm.ac.uk.
Piero L OlliaroCentre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, UK.
Debrah I BoerasGlobal Health Impact Group, Atlanta, GA, USA.
Noah FongwenDepartment of Clinical Research, London School of Hygiene and Tropical Medicine, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

WHO recommends a minimum of 80% sensitivity and 97% specificity for antigen-detection rapid diagnostic tests (Ag-RDTs), which can be used for patients with symptoms consistent with COVID-19. However, after the acute phase when viral load decreases, use of Ag-RDTs might lead to high rates of false negatives, suggesting that the tests should be replaced by a combination of molecular and serological tests. When the likelihood of having COVID-19 is low, such as for asymptomatic individuals in low prevalence settings, for travel, return to schools, workplaces, and mass gatherings, Ag-RDTs with high negative predictive values can be used with confidence to rule out infection. For those who test positive in low prevalence settings, the high false positive rate means that mitigation strategies, such as molecular testing to confirm positive results, are needed. Ag-RDTs, when used appropriately, are promising tools for scaling up testing and ensuring that patient management and public health measures can be implemented without delay.

Indexed as

SARS-CoV-2COVID-19COVID-19 TestingFalse Negative ReactionsFalse Positive ReactionsHumansPredictive Value of TestsSensitivity and Specificity

Identifiers

PMID33636148
PMCPMC7906660

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.