Evidence map›Paper›PMID 36002806›Full record

ArticleBMC medical imaging2022

The effect of a training webinar on decreasing inter-observer variability in making a radiologic diagnosis of bronchiectasis.

O'Neil Green, Alexander Knee, Angelica Patino, Lucy Modahl, Sybille Liautaud

Open access · goldAbstract read
In one paragraph

Article in BMC medical imaging, 2022. 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
0.2field-weighted citation impact, top 50% 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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. 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

5 authors at 2 institutions in 1 country.

O'Neil GreenUMASS-Baystate, 759 Chestnut St, Springfield, MA, 01144, USA. oneil.greenmd@baystatehealth.org.
Alexander KneeDepartment of Healthcare Delivery and Population Science, Baystate Medical Center, 759 Chestnut St, Springfield, MA, 01144, USA.
Angelica PatinoDepartment of Radiology, UMASS-Baystate, 759 Chestnut St, Springfield, MA, 01144, USA.
Lucy ModahlDepartment of Radiology, UMASS-Baystate, 759 Chestnut St, Springfield, MA, 01144, USA.
Sybille LiautaudDepartment of Pulmonary and Critical Care Medicine, UMASS-Baystate, 759 Chestnut St, Springfield, MA, 01144, USA.
Baystate Health · USBaystate Medical Center · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Non-cystic fibrosis bronchiectasis is a clinically important disease with an estimated 340,000-522,000 persons living with the disease and 70,000 being diagnosed annually. The radiographic diagnosis remains a pivotal part of recognizing the disease due to its protean clinical manifestations. As physicians are sensitized to this disease, a greater proportion of patients are being diagnosed with mild to moderate bronchiectasis. Despite the established use of CT chest as the main tool for making a radiologic diagnosis of bronchiectasis, the literature supporting the process of making that diagnosis is somewhat sparse. Concurrently, there has been an increased trend to have Web-based radiologic tutorials due to its convenience, the ability of the learner to set the pace of learning and the reduced cost compared to in-person learning. The COVID-19 pandemic has accelerated this trend. We wanted to look carefully at the effect of a Web-based training session on interrater reliability. Agreement was calculated as percentages and kappa and prevalence adjusted kappa calculated. We found that a single Web-based training session had little effect on the variability and accuracy of diagnosis of bronchiectasis. Larger studies are needed in this area with multiple training sessions.

Indexed as

BronchiectasisCOVID-19COVID-19 TestingHumansObserver VariationPandemicsReproducibility of ResultsBronchiectasisCT chestInterpretationInterrater reliabilityWeb-based training

Identifiers

PMID36002806
PMCPMC9399978
OpenAlexW4292884059

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.