Evidence map›Paper›PMID 34630913›Full record

ReviewWorld journal of radiology2021

Comprehensive literature review on the radiographic findings, imaging modalities, and the role of radiology in the COVID-19 pandemic.

Aman Pal, Abulhassan Ali, Timothy R Young, Juan Oostenbrink, Akul Prabhakar, Amogh Prabhakar, Nina Deacon, Amar Arnold, Ahmed Eltayeb, Charles Yap and 6 more

Open access · diamondAbstract readReview
In one paragraph

Review in World journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

9 citing papers in PubMed, 14 citations in OpenAlex.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Observational
  6. Article
  7. Review
  8. What Is COVID 19 Teaching Us about Pulmonary Ultrasound?Diagnostics (Basel, Switzerland) · 2022
    Review
  9. 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

16 authors at 4 institutions in 3 countries.

Aman PalSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Abulhassan AliSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Timothy R YoungSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Juan OostenbrinkSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Akul PrabhakarSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Amogh PrabhakarSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Nina DeaconSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Amar ArnoldSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Ahmed EltayebSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Charles YapSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
David M YoungDepartment of Computer Science, Yale University, New Haven, CO 06520, United States.
Alan TangDepartment of Health Science, Duke University, Durham, NC 27708, United States.
Subramanian LakshmananSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Ying Yi LimSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland.
Martha PokarowskiThe Hospital for Sick Kids, University of Toronto, Toronto M5S, Ontario, Canada.
Pramath KakodkarSchool of Medicine, National University of Ireland Galway, Galway H91 TK33, Galway, Ireland. p.kakodkar1@nuigalway.ie.
Duke University · USHospital for Sick Children · CAUniversity of Toronto · CAOllscoil na Gaillimhe – University of Galway · IE

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the outbreak of the coronavirus disease 2019 (COVID-19) pandemic, over 103214008 cases have been reported, with more than 2231158 deaths as of January 31, 2021. Although the gold standard for diagnosis of this disease remains the reverse-transcription polymerase chain reaction of nasopharyngeal and oropharyngeal swabs, its false-negative rates have ignited the use of medical imaging as an important adjunct or alternative. Medical imaging assists in identifying the pathogenesis, the degree of pulmonary damage, and the characteristic features in each imaging modality. This literature review collates the characteristic radiographic findings of COVID-19 in various imaging modalities while keeping the preliminary focus on chest radiography, computed tomography (CT), and ultrasound scans. Given the higher sensitivity and greater proficiency in detecting characteristic findings during the early stages, CT scans are more reliable in diagnosis and serve as a practical method in following up the disease time course. As research rapidly expands, we have emphasized the CO-RADS classification system as a tool to aid in communicating the likelihood of COVID-19 suspicion among healthcare workers. Additionally, the utilization of other scoring systems such as MuLBSTA, Radiological Assessment of Lung Edema, and Brixia in this pandemic are reviewed as they integrate the radiographic findings into an objective scoring system to risk stratify the patients and predict the severity of disease. Furthermore, current progress in the utilization of artificial intelligence

Indexed as

Brixia scoreComputed tomographyCoronavirusCOVID-19MuLBSTA Scoring systemRadiological Assessment of Lung Edema classificationUltrasound

Identifiers

PMID34630913
PMCPMC8473437
OpenAlexW3203762518

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.