Evidence map›Paper›PMID 33063040›Full record

ReviewCritical care explorations2020

Molecular Diagnosis of Coronavirus Disease 2019.

Claudia C Dos Santos, Barbara A Zehnbauer, Uriel Trahtemberg, John Marshall

Open access · goldAbstract readReview
In one paragraph

Review in Critical care explorations, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
0.2field-weighted citation impact, top 39% of its field
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

4 citing papers in PubMed, 6 citations in OpenAlex.

  1. Article
  2. Article
  3. Review
  4. Review
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 at 3 institutions in 2 countries.

Claudia C Dos SantosKeenan Research Center for Biomedical Research, Unity Health Toronto, Toronto, ON, Canada.
Barbara A ZehnbauerDepartment of Pathology, Emory University School of Medicine, Atlanta, GA.
Uriel TrahtembergKeenan Research Center for Biomedical Research, Unity Health Toronto, Toronto, ON, Canada.
John MarshallKeenan Research Center for Biomedical Research, Unity Health Toronto, Toronto, ON, Canada.
Unity Health TorontoEmory University · USUniversity of Toronto · CA

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesTo review molecular diagnostics for coronavirus disease 2019. The world is in the midst of a coronavirus disease 2019 pandemic. Containing the spread of the severe acute respiratory distress coronavirus is critical. Instrumental to the future success is the ability to reliably and reproducibly detect this inciting pathogen to inform public health containment policies and treatment decisions. DATA SOURCES: Molecular diagnostics focusing on molecular detection methodologies for detection of the virus and the presence of the disease. STUDY SELECTION: Narrative review. DATA EXTRACTION: Literature, PubMed, Scopus, and official government documents. DATA SYNTHESIS: Diagnosing severe acute respiratory syndrome coronavirus is done through real-time reverse transcriptase-polymerase chain reaction tests, cell culture, and serology. For patients, diagnostics are an integral part of a full medical history, physical examinations, blood tests, and diagnostic imaging.

conclusionsHere, we review current approaches to the molecular diagnosis of coronavirus disease 2019.

Indexed as

coronavirus disease 2019coronavirus disease 2019 serologic testsnucleic acid amplification tests; real-time reverse transcription polymerase chain reaction diagnostic primer panelsevere acute respiratory syndrome coronavirus 2

Identifiers

PMID33063040
PMCPMC7515613
OpenAlexW3092830819

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
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.