Evidence map›Paper›PMID 32733707›Full record

ReviewRadiology research and practice2020

A Concise Review and Required Precautions for COVID-19 Outbreak in Diagnostic and Interventional Radiology.

Ali Kord, Behnam Rabiee, Siwen Wang, Sara Rostami, Ron C Gaba, Karen L Xie

Open access · goldAbstract readReview
In one paragraph

Review in Radiology research and practice, 2020. 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
0.6field-weighted citation impact, top 36% 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

2 citing papers in PubMed, 9 citations in OpenAlex.

  1. Article
  2. Observational
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 at 1 institution in 1 country.

Ali KordDepartment of Radiology, University of Illinois at Chicago, Chicago, IL, USA.ORCID https://orcid.org/0000-0003-1913-4087
Behnam RabieeDepartment of Radiology, University of Illinois at Chicago, Chicago, IL, USA.
Siwen WangBiomedical Visualization Graduate Program, University of Illinois at Chicago, Chicago, IL, USA.
Sara RostamiDepartment of Radiology, University of Illinois at Chicago, Chicago, IL, USA.
Ron C GabaDepartment of Radiology, University of Illinois at Chicago, Chicago, IL, USA.ORCID https://orcid.org/0000-0002-6601-1576
Karen L XieDepartment of Radiology, University of Illinois at Chicago, Chicago, IL, USA.ORCID https://orcid.org/0000-0003-1373-2975
University of Illinois Chicago · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

A global outbreak of a novel coronavirus (COVID-19) pneumonia began in December 2019 in Wuhan, China. The World Health Organization (WHO) announced a pandemic on March 11, 2020. The rapid rise in the case numbers and mortality led to the saturation of hospitals in many countries. COVID-19 patients usually present with fever, fatigue, dry cough, and dyspnea. Given the shortage of diagnostic kits in many countries and very high sensitivity of computed tomography (CT) for diagnosis of COVID-19 in clinically suspicious patients, the chest CT has been implemented among the primary initial methods of diagnosis before the confirmatory laboratory tests. This puts radiologists and radiology staff on the front line of this alarming pandemic. This report summarizes chest CT findings of COVID-19 patients to facilitate diagnosis and reviews a list of necessary precautions and safety measures for diagnostic and interventional radiology personnel. These precautionary plans are extremely important to avoid contamination of the health-care providers, as well as cross-contamination between patients.

Identifiers

PMID32733707
PMCPMC7376426
OpenAlexW3045319873

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.