Evidence map›Paper›PMID 30730757›Full record

ArticleThe British journal of radiology2019

Internet-based measurement of visual assessment skill of trainee radiologists: developing a sensitive tool.

Martin Thirkettle, Mandela Thyoka, Padmini Gopalan, Nadiah Fernandes, Tom Stafford, Amaka C Offiah

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Article in The British journal of radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Martin Thirkettle1 Department of Psychology, Sociology & Politics, Sheffield Hallam University , Sheffield , United Kingdom.ORCID http://orcid.org/0000-0002-6200-3130
Mandela Thyoka2 Department of Radiology, Sheffield Teaching Hospitals , Sheffield , United Kingdom.
Padmini Gopalan2 Department of Radiology, Sheffield Teaching Hospitals , Sheffield , United Kingdom.
Nadiah Fernandes3 Imperial College London School of Medicine , London , United Kingdom.
Tom Stafford4 Department of Psychology, University of Sheffield , Sheffield , United Kingdom.
Amaka C Offiah5 Department of Radiology, Sheffield Children's NHS Foundation Trust , Sheffield , United Kingdom.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveExpert radiologists exhibit high levels of visual diagnostic accuracy from review of radiological images, doing so after accumulating years of training and experience. To train new radiologists, learning interventions must focus on the development of these skills. By developing a web-based measure of image assessment, a key part of visual diagnosis, we aimed to capture differences in the performance of expert, trainee and non-radiologists.

methods12 consultant paediatric radiologists, 12 radiology registrars, and 39 medical students were recruited to the study. All participants completed a two-part, online task requiring them to visually assess 30 images (25 containing an abnormality) drawn from a library of 150 paediatric skeletal radiographs assessed prior to the study. Participants first identified whether an image contained an abnormality, and then clicked within the image to mark its location. Performance measures of identification accuracy, localisation precision, and task time were collected.

resultsDespite the difficulties of web-based testing, large differences in performance, both in terms of the accuracy of abnormality identification and in the precision of abnormality localisation were found between groups, with consultant radiologists the most accurate both at identifying images containing abnormalities (

conclusionsOur data demonstrate that an online measurement of radiological skill is sufficiently sensitive to detect group level changes in performance consistent with the development of expertise. ADVANCES IN KNOWLEDGE: The developed tool will allow future studies assessing the impact of different training strategies on cognitive performance and diagnostic accuracy.

Indexed as

Clinical CompetenceInternetChildEducational MeasurementHumansRadiologySensitivity and SpecificityStudents, Medical

Identifiers

PMID30730757
PMCPMC6580899

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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.