Evidence map›Paper›PMID 33121625›Full record

ReviewClinics in laboratory medicine2020

Measuring the Serologic Response to Severe Acute Respiratory Syndrome Coronavirus 2: Methods and Meaning.

Nicole D Pecora, Martin S Zand

Abstract readReview
In one paragraph

Review in Clinics in laboratory medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

0numbers the graph read from it
0cells of the map it votes in
10citing 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

10 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Review
  7. Article
  8. Article
  9. Review
  10. 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

2 authors.

Nicole D PecoraUniversity of Rochester Medical Center, Rochester, NY, USA. Electronic address: Nicole_pecora@urmc.rochester.edu.
Martin S ZandDepartment of Medicine, Nephrology (SMD), Clinical & Translational Science Institute, Clinical Research University of Rochester Medical Center, School of Medicine and Dentistry, 601 Elmwood Avenue, Box 675, Rochester, NY 14642, USA.

Funding

The University of Rochester's Clinical and Translational Science InstituteUL1TR002001 · NCATS · UNIVERSITY OF ROCHESTER · PI WILSON, KAREN M., ZAND, MARTIN S · 2016 to 2024
$34.6M
Modeling Mechanisms of Adjuvanted Influenza Vaccine Induced IgG Repertoire Diversity and Heterosubtypic ImmunityR01AI129518 · NIAID · UNIVERSITY OF ROCHESTER · PI HYRIEN, OLLIVIER, ZAND, MARTIN S · 2017 to 2021
$3.5M
Predictive Modeling of Multidimensional Anti-Influenza IgG RepertoiresR21AI138500 · NIAID · UNIVERSITY OF ROCHESTER · PI ZAND, MARTIN S · 2018 to 2019
$424k
NCATS NIH HHS UL1 TR002001NIAID NIH HHS R01 AI129518NIAID NIH HHS R21 AI138500
6 · The paper itself

Abstract

The entire spectrum of diagnostic testing, from reagent supply to test performance, has been a major focus during the coronavirus disease 2019 (COVID-19) pandemic. The hope for serologic testing is that it will provide both epidemiologic information about seroprevalence as well as individual information about previous infection. This information is particularly helpful for high-risk individuals who may be outside of the viral shedding window, such as children with suspected multisystem inflammatory syndrome. It is not yet understood whether serologic testing can be interpreted in terms of protective immunity. These concerns must be addressed using highly sensitive and specific tests.

Indexed as

BetacoronavirusCoronavirus InfectionsPandemicsPneumonia, ViralSeroepidemiologic StudiesClinical Laboratory TechniquesCOVID-19COVID-19 TestingHumansImmunity, HumoralProtective FactorsSARS-CoV-2Sensitivity and SpecificityCOVID-19COVID-19 serologySARS-CoV-2

Identifiers

PMID33121625
PMCPMC7414299

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.