Evidence map›Paper›PMID 32538893›Full record

ReviewJournal of X-ray science and technology2020

Clinical and radiological features of novel coronavirus pneumonia.

Qiuting Zheng, Yibo Lu, Fleming Lure, Stefan Jaeger, Puxuan Lu

Open access · bronzeAbstract readReview
In one paragraph

Review in Journal of X-ray science and technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.

0numbers the graph read from it
0cells of the map it votes in
26citing papers in PubMed
6.3field-weighted citation impact, top 2% 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

26 citing papers in PubMed, 66 citations in OpenAlex.

  1. Trial
  2. Article
  3. Article
  4. Article
  5. Article
  6. Observational
  7. Correlation of important prognostic factors and CT scores in invasive and non-invasive ventilation of COVID-19 patients.Ulusal travma ve acil cerrahi dergisi = Turkish journal of trauma & emergency surgery : TJTES · 2023
    Review
  8. Article
  9. Article
  10. Article
  11. Disease Progression Detection via Deep Sequence Learning of Successive Radiographic Scans.International journal of environmental research and public health · 2022
    Article
  12. CT Imaging Research Progress in COVID-19.Current medical imaging · 2022
    Review
  13. Article
  14. Article
  15. Observational
  16. COVID-19 lungs in post-mortem computed tomography.Rechtsmedizin (Berlin, Germany) · 2021
    Article
  17. Article
  18. Review
  19. Article
  20. 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

5 authors at 4 institutions in 2 countries.

Qiuting ZhengDepartment of Medical Imaging, Shenzhen Center for Chronic Disease Control, Guangdong Shenzhen 518020, China.
Yibo LuDepartment of Medical Imaging, The Fourth People's Hospital of Nanning, Guangxi Nanning 530023, China.
Fleming LureMS Technologies, 10110 Molecular Dr., Suite 305, Rockville, MD 20850, USA.
Stefan JaegerNational Library of Medicine, National Institutes of Health, 8600 Rockville Pike, Bethesda, MD 20894, USA.
Puxuan LuDepartment of Medical Imaging, Shenzhen Center for Chronic Disease Control, Guangdong Shenzhen 518020, China.
Shenzhen Nanshan Center for Chronic Disease Control · CNNational Institutes of Health · USSA Technologies (United States) · USThe Fourth People's Hospital · CN

Funding

Machine learning and artificial intelligence algorithms for chest imaging diagnosticsZIALM010004 · NLM · NATIONAL LIBRARY OF MEDICINE · PI ANTANI, SAMEER · 2016 to 2020
$2.8M
6 · The paper itself

Abstract

Recently, COVID-19 has spread in more than 100 countries and regions around the world, raising grave global concerns. COVID-19 transmits mainly through respiratory droplets and close contacts, causing cluster infections. The symptoms are dominantly fever, fatigue, and dry cough, and can be complicated with tiredness, sore throat, and headache. A few patients have symptoms such as stuffy nose, runny nose, and diarrhea. The severe disease can progress rapidly into the acute respiratory distress syndrome (ARDS). Reverse transcription polymerase chain reaction (RT-PCR) and Next-generation sequencing (NGS) are the gold standard for diagnosing COVID-19. Chest imaging is used for cross validation. Chest CT is highly recommended as the preferred imaging diagnosis method for COVID-19 due to its high density and high spatial resolution. The common CT manifestation of COVID-19 includes multiple segmental ground glass opacities (GGOs) distributed dominantly in extrapulmonary/subpleural zones and along bronchovascular bundles with crazy paving sign and interlobular septal thickening and consolidation. Pleural effusion or mediastinal lymphadenopathy is rarely seen. In CT imaging, COVID-19 manifests differently in its various stages including the early stage, the progression (consolidation) stage, and the absorption stage. In its early stage, it manifests as scattered flaky GGOs in various sizes, dominated by peripheral pulmonary zone/subpleural distributions. In the progression state, GGOs increase in number and/or size, and lung consolidations may become visible. The main manifestation in the absorption stage is interstitial change of both lungs, such as fibrous cords and reticular opacities. Differentiation between COVID-19 pneumonia and other viral pneumonias are also analyzed. Thus, CT examination can help reduce false negatives of nucleic acid tests.

Indexed as

BetacoronavirusCoronavirus InfectionsCOVID-19Diagnosis, DifferentialDisease ProgressionHumansLungPandemicsPleural EffusionPneumonia, ViralReal-Time Polymerase Chain ReactionSARS-CoV-2Tomography, X-Ray Computed2019-novel Coronaviruses (2019-nCoV)Computed Tomography (CT)COVID-19differentiation of pneumoniaground glass opacity (GGO)SARS-CoV-2

Identifiers

PMID32538893
PMCPMC7369043
OpenAlexW3034228803

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.