Evidence map›Paper›PMID 34054967›Full record

ArticleCase reports in medicine2021

The Dynamics of SARS-CoV-2 (RT-PCR) Testing.

Nicole Joyce, Lynsey Seim, Michael Smerina

Abstract readCase Reports
In one paragraph

Article in Case reports in medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Article
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

3 authors.

Nicole JoyceAssistant Professor of Medicine, Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL, USA.ORCID https://orcid.org/0000-0002-5207-9350
Lynsey SeimAssistant Professor of Medicine, Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL, USA.ORCID https://orcid.org/0000-0001-5366-1126
Michael SmerinaAssistant Professor of Medicine, Hospital Internal Medicine, Mayo Clinic, Jacksonville, FL, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The COVID-19 pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is estimated to have affected 6.2 million people in the United States and 27.5 million people worldwide as of September 9, 2020. On February 2, 2020, the Secretary of the Department of Health and Human Services (HHS) determined that the public health emergency justified the development and emergency use of "in vitro diagnostics for the detection and/or diagnosis of the virus that causes COVID-19" by activating the Emergency Use Authorization (EUA) authority under section 564 of the Federal Food, Drug, and Cosmetic Act. Unfortunately, effective mitigation efforts were thwarted early in the outbreak resulting in an expansion of the initial EUA on February 29, 2020, to improve accessibility to in vitro diagnostic testing. Expectantly, the development and deployment of SARS-CoV-2 testing including RT-PCR expanded rapidly in the weeks following the EUA expansion. These newly developed and approved SARS-CoV-2 RT-PCR tests boast impressive positive and negative agreement rates nearing 100%. Despite the exceptionally high rates of agreement, caution is advised as the RT-PCR tests approved under the COVID-19 EUA are in vitro analyses developed with samples artificially doped with SARS-CoV-2 RNA. These tests therefore do not have clinically applicable sensitivity and specificity because they lack a "gold standard" for diagnosis. Here we present three challenging cases requiring cautious interpretation of the newest generation of RT-PCR molecular detection assays, highlighting the major challenges faced by providers treating patients potentially infected with SARS-CoV-2.

Identifiers

PMID34054967
PMCPMC8142808

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Registered trials

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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.