Evidence map›Paper›PMID 40770759›Full record

ArticleBMC medical education2025

Improving the accuracy of emergency department clinicians in detecting SARS-COV-2 on chest X-rays using a bespoke virtual training platform.

Jasdeep Bahra, Anita Acharya, Sarim Ather, Rachel Benamore, Julie-Ann Moreland, Divyansh Gulati, Lee How, Thandiwe Rosemarysdottir, Miranthi Huwae, Sarah Wilson and 6 more

Abstract readMulticenter Study
In one paragraph

Article in BMC medical education, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

16 authors.

Jasdeep BahraEmergency Medicine Research Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford, OX3 9DU, UK.
Anita AcharyaEmergency Medicine Research Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford, OX3 9DU, UK.
Sarim AtherRadiology Department, Oxford University Hospitals, Oxford, UK.
Rachel BenamoreRadiology Department, Oxford University Hospitals, Oxford, UK.
Julie-Ann MorelandRadiology Department, Oxford University Hospitals, Oxford, UK.
Divyansh GulatiMilton Keynes University Hospital, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.
Lee HowMilton Keynes University Hospital, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.
Thandiwe RosemarysdottirMilton Keynes University Hospital, Milton Keynes University Hospital NHS Foundation Trust, Milton Keynes, UK.
Miranthi HuwaeWexham Park Hospital, Frimley Health NHS Foundation Trust, Slough, UK.
Sarah WilsonWexham Park Hospital, Frimley Health NHS Foundation Trust, Slough, UK.
Abhishek BanerjiStoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
Katerina MansoStoke Mandeville Hospital, Buckinghamshire Healthcare NHS Trust, Aylesbury, UK.
Liza KeatingRoyal Berkshire Hospital, Royal Berkshire NHS Foundation Trust, Reading, UK.
Amy BarrettRoyal Berkshire Hospital, Royal Berkshire NHS Foundation Trust, Reading, UK.
Fergus GleesonRadiology Department, Oxford University Hospitals, Oxford, UK.
Alex NovakEmergency Medicine Research Oxford (EMROx), Oxford University Hospitals NHS Foundation Trust, Headley Way, Oxford, OX3 9DU, UK. Alex.novak@ouh.nhs.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDuring and after the COVID pandemic, online learning became a key component in most undergraduate and post-graduate training. The non-specific symptoms of SARS-CoV-2 and limitations of available diagnostic tests can make it difficult to detect and diagnose in acute care settings. Accurate identification of SARS-CoV-2 related changes on chest x-ray (CXR) by frontline clinicians involved in direct patient care in the Emergency Department (ED) is an important skill. We set out to measure the accuracy of ED clinicians in detecting SARS-CoV-2 changes on CXRs and assess whether this could be improved using an online learning platform.

methodsBaseline reporting performance of a multi-centre cohort of ED clinicians with varying experience was assessed via the Report and Image Quality Control (RAIQC) online platform. Emergency Medicine clinicians working in EDs across five hospitals in the Thames Valley Emergency medicine Research Network (TaVERN) region were recruited over a six-month period. An image bank was created containing both SARS-CoV-2 and non- SARS-CoV-2 pathological findings. Radiological ground truth diagnosis was established by thoracic radiologists and corroborated by RT- PCR results. Participants then undertook an online training module with performance re-assessed. Diagnostic accuracy and speed of X-ray reporting was assessed before and after training in 3 subgroups: Consultants, Junior Doctors and Nurses.

results90 clinicians undertook pre-training assessment and 56 undertook post training assessment. There was an overall improved reporting accuracy for participants who undertook both pre and post training assessments from (44.0±10.5%) to 57.4% (±9.39)% (p < 0.001). The sensitivity for recognition of SARS-CoV-2 improved from 64.8 to 76.8%.

conclusionED clinicians show moderate baseline accuracy in the identification of SARS-CoV-2 related changes on CXR. Accuracy and speed can be improved by online training.

Indexed as

Clinical CompetenceCOVID-19Education, DistanceEmergency MedicineEmergency Service, HospitalRadiography, ThoracicHumansSARS-CoV-2Chest X-rayCOVID-19Diagnostic accuracyEmergency departmentRadiographySARS-CoV-2

Identifiers

PMID40770759
PMCPMC12326861

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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.