Evidence map›Paper›PMID 35707747›Full record

ArticleGlobal health, epidemiology and genomics2022

SARS-CoV-2 Genome-Based Severity Predictions Correspond to Lower qPCR Values and Higher Viral Load.

Martin Skarzynski, Erin M McAuley, Ezekiel J Maier, Anthony C Fries, Jameson D Voss, Richard R Chapleau

Abstract read
In one paragraph

Article in Global health, epidemiology and genomics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
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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

6 authors.

Martin SkarzynskiBooz Allen Hamilton, Bethesda, MD 20814, USA.
Erin M McAuleyBooz Allen Hamilton, Bethesda, MD 20814, USA.ORCID 0000-0003-3911-4390
Ezekiel J MaierBooz Allen Hamilton, Bethesda, MD 20814, USA.ORCID 0000-0003-1216-6537
Anthony C FriesUS Air Force School of Aerospace Medicine, Wright Patterson AFB, OH 45433, USA.ORCID 0000-0001-7556-1353
Jameson D VossUS Air Force Medical Readiness Agency, Falls Church, VA 22042, USA.ORCID 0000-0002-4104-1744
Richard R ChapleauUS Air Force School of Aerospace Medicine, Wright Patterson AFB, OH 45433, USA.ORCID 0000-0001-6610-5946

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The 2019 coronavirus disease (COVID-19) pandemic has demonstrated the importance of predicting, identifying, and tracking mutations throughout a pandemic event. As the COVID-19 global pandemic surpassed one year, several variants had emerged resulting in increased severity and transmissibility. Here, we used PCR as a surrogate for viral load and consequent severity to evaluate the real-world capabilities of a genome-based clinical severity predictive algorithm. Using a previously published algorithm, we compared the viral genome-based severity predictions to clinically derived PCR-based viral load of 716 viral genomes. For those samples predicted to be "severe" (probability of severe illness >0.5), we observed an average cycle threshold (Ct) of 18.3, whereas those in in the "mild" category (severity probability <0.5) had an average Ct of 20.4 (

Indexed as

COVID-19SARS-CoV-2HumansReal-Time Polymerase Chain ReactionSeverity of Illness IndexViral Load

Identifiers

PMID35707747
PMCPMC9173902

What OpenQuestion holds

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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.