Evidence map›Paper›PMID 38217064›Full record

ArticleEmerging infectious diseases2024

Model for Interpreting Discordant SARS-CoV-2 Diagnostic Test Results.

Oluwaseun F Egbelowo, Spencer J Fox, Graham C Gibson, Lauren Ancel Meyers

Abstract read
In one paragraph

Article in Emerging infectious diseases, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Oluwaseun F Egbelowo
Spencer J Fox
Graham C Gibson
Lauren Ancel Meyers

Funding

Accelerating viral outbreak detection in US cities using mechanistic models, machine learning and diverse geospatial dataR01AI151176 · NIAID · YALE UNIVERSITY · PI GALVANI, ALISON P, MEYERS, LAUREN ANCEL · 2020 to 2024
$3.9M
RFA-IP-22-004, Modeling toolkit to evaluate multifaceted control strategies for seasonal and pandemic influenzaU01IP001136 · IP · UNIVERSITY OF TEXAS AT AUSTIN · PI MEYERS, LAUREN ANCEL · 2020 to 2024
$3.4M
ACL HHS U01IP001136NCIRD CDC HHS U01 IP001136NIAID NIH HHS R01 AI151176
6 · The paper itself

Abstract

We devised a model to interpret discordant SARS-CoV-2 test results. We estimate that, during March 2020-May 2022, a patient in the United States who received a positive rapid antigen test result followed by a negative nucleic acid test result had only a 15.4% (95% CI 0.6%-56.7%) chance of being infected.

Indexed as

COVID-19SARS-CoV-2COVID-19 TestingDiagnostic Tests, RoutineHumansSensitivity and SpecificityUnited Statescoronaviruscoronavirus diseaseCOVID-19diagnostic testPCRrapid antigen testrespiratory infectionsSARSSARS-CoV-2severe acute respiratory syndrome coronavirus 2viruseszoonoses

Identifiers

PMID38217064
PMCPMC10826750

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.