Evidence map›Paper›PMID 36282308›Full record

ArticleEuropean radiology2023

Chest radiograph classification and severity of suspected COVID-19 by different radiologist groups and attending clinicians: multi-reader, multi-case study.

Arjun Nair, Alexander Procter, Steve Halligan, Thomas Parry, Asia Ahmed, Mark Duncan, Magali Taylor, Manil Chouhan, Trevor Gaunt, James Roberts and 13 more

Abstract read
In one paragraph

Article in European radiology, 2023. 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
–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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

23 authors.

Arjun NairDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK. arjun.nair1@nhs.net.ORCID http://orcid.org/0000-0001-9270-3771
Alexander ProcterDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Steve HalliganCentre for Medical Imaging, University College London, UCL Centre for Medical Imaging, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
Thomas ParryCentre for Medical Imaging, University College London, UCL Centre for Medical Imaging, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
Asia AhmedDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Mark DuncanDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Magali TaylorDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Manil ChouhanDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Trevor GauntDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
James RobertsDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Niels van VuchtDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Alan CampbellDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Laura May DavisDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Joseph JacobCentre for Medical Image Computing, Department of Computer Science, University College London, 90 High Holborn, Floor 1, London, WC1V 6LJ, UK.
Rachel HubbardDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Shankar KumarDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Ammaarah SaidDepartment of Radiology, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Xinhui ChanDepartment of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Tim CutfieldDepartment of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Akish LuintelDepartment of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Michael MarksDepartment of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Neil StoneDepartment of Tropical and Infectious Diseases, University College London Hospital, 235 Euston Road, London, NW1 2BU, UK.
Sue MalletCentre for Medical Imaging, University College London, UCL Centre for Medical Imaging, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.

Funding

Wellcome Trust 209553/Z/17/Z
6 · The paper itself

Abstract

objectivesTo quantify reader agreement for the British Society of Thoracic Imaging (BSTI) diagnostic and severity classification for COVID-19 on chest radiographs (CXR), in particular agreement for an indeterminate CXR that could instigate CT imaging, from single and paired images.

methodsTwenty readers (four groups of five individuals)-consultant chest (CCR), general consultant (GCR), and specialist registrar (RSR) radiologists, and infectious diseases clinicians (IDR)-assigned BSTI categories and severity in addition to modified Covid-Radiographic Assessment of Lung Edema Score (Covid-RALES), to 305 CXRs (129 paired; 2 time points) from 176 guideline-defined COVID-19 patients. Percentage agreement with a consensus of two chest radiologists was calculated for (1) categorisation to those needing CT (indeterminate) versus those that did not (classic/probable, non-COVID-19); (2) severity; and (3) severity change on paired CXRs using the two scoring systems.

resultsAgreement with consensus for the indeterminate category was low across all groups (28-37%). Agreement for other BSTI categories was highest for classic/probable for the other three reader groups (66-76%) compared to GCR (49%). Agreement for normal was similar across all radiologists (54-61%) but lower for IDR (31%). Agreement for a severe CXR was lower for GCR (65%), compared to the other three reader groups (84-95%). For all groups, agreement for changes across paired CXRs was modest.

conclusionAgreement for the indeterminate BSTI COVID-19 CXR category is low, and generally moderate for the other BSTI categories and for severity change, suggesting that the test, rather than readers, is limited in utility for both deciding disposition and serial monitoring. KEY POINTS: • Across different reader groups, agreement for COVID-19 diagnostic categorisation on CXR varies widely. • Agreement varies to a degree that may render CXR alone ineffective for triage, especially for indeterminate cases. • Agreement for serial CXR change is moderate, limiting utility in guiding management.

Indexed as

COVID-19HumansRadiographyRadiography, ThoracicRadiologistsReproducibility of ResultsRetrospective StudiesCoronavirusDiagnosisObserver variationX-ray

Identifiers

PMID36282308
PMCPMC9592875

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.