Evidence map›Paper›PMID 41560383›Full record

ArticleYonsei medical journal2026

Distinguishing Takayasu Arteritis and Giant Cell Arteritis Based on Large-Vessel Involvement Patterns.

Oh Chan Kwon, Jang Woo Ha, Min-Chan Park, Yong-Beom Park, Sang-Won Lee

Abstract read
In one paragraph

Article in Yonsei medical journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
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0citing papers in PubMed
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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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Oh Chan Kwon *Division of Rheumatology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0001-7962-3697
Jang Woo Ha *Division of Rheumatology, Department of Internal Medicine, Yongin Severance Hospital, Yonsei University College of Medicine, Yongin, Korea.ORCID https://orcid.org/0000-0002-3307-5215
Min-Chan ParkDivision of Rheumatology, Department of Internal Medicine, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-1189-7637
Yong-Beom ParkDivision of Rheumatology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0003-4695-8620
Sang-Won LeeDivision of Rheumatology, Department of Internal Medicine, Severance Hospital, Yonsei University College of Medicine, Seoul, Korea.ORCID https://orcid.org/0000-0002-8038-3341

Funding

Chong Kun Dang Pharmaceutical Corp. 4-2022-1351Eisai Korea 4-2024-0700
6 · The paper itself

Abstract

purposeTakayasu arteritis (TAK) and extracranial large-vessel (LV) giant cell arteritis (GCA) share overlapping features, making differential diagnosis between the two diseases challenging. We aimed to identify LV involvement patterns that could accurately differentiate TAK and GCA. MATERIALS AND

methodsThis retrospective cohort study included 181 patients (TAK, n=175; GCA, n=6). LV involvement patterns were assessed using computed tomography (CT) and/or ¹⁸F-fluorodeoxyglucose positron emission tomography/CT performed at diagnosis. A multivariable logistic regression model was used to identify LV involvement patterns that accurately distinguish TAK and GCA. Area under the curve (AUC) was estimated to determine the accuracy.

resultsThe right subclavian artery (30.3% vs. 83.3%,

conclusionDistinct patterns of LV involvement, particularly aortic arch involvement, either alone or combined with right subclavian artery involvement, could accurately differentiate TAK and GCA.

Indexed as

Giant Cell ArteritisTakayasu ArteritisAgedAorta, ThoracicDiagnosis, DifferentialFemaleHumansLogistic ModelsMaleMiddle AgedPositron Emission Tomography Computed TomographyRetrospective StudiesSubclavian ArteryTomography, X-Ray Computeddifferential diagnosisgiant cell arteritislarge vesselsTakayasu arteritis

Identifiers

PMID41560383
PMCPMC12880768

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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.