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Record W3093461202 · doi:10.1186/s13075-020-02335-4

Magnetic resonance imaging compared to ultrasonography in giant cell arteritis: a cross-sectional study

2020· article· en· W3093461202 on OpenAlexaff
Ashley Yip, Elizabeth Jernberg, Mohammad Bardi, Julia Geiger, Frode Lohne, Wolfgang Schmidt, Geirmund Myklebust, Andreas P. Diamantopoulos

Bibliographic record

VenueArthritis Research & Therapy · 2020
Typearticle
Languageen
FieldMedicine
TopicVasculitis and related conditions
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGiant cell arteritisMedicineMagnetic resonance imagingMagnetic resonance angiographyVasculitisRheumatologyRadiologyArteritisTakayasu's arteritisInternal medicineDisease

Abstract

fetched live from OpenAlex

BACKGROUND: There has been a shift in recent years to using ultrasound (US) and magnetic resonance imaging (MRI) as first-line investigations for suspected cranial large vessel vasculitis (LVV) and is a new recommendation by the EULAR 2018 guidelines for imaging in LVV. This cross-sectional study compares the performance of US and MRI and contrast-enhanced magnetic resonance angiography (MRA) for detecting vasculitis in patients with giant cell arteritis (GCA). METHODS: Patients with new-onset or already diagnosed GCA were recruited. The common temporal arteries and supra-aortic large vessels were evaluated by US and MRI/MRA. Blinded experts read the images and applied a dichotomous score (vasculitis: yes/no) in each vessel. RESULTS: Thirty-seven patients with giant cell arteritis (GCA) were recruited. Two patients were excluded. Of the remaining patients, nine had new-onset disease and 26 had established disease. Mean age was 71 years, and median C-reactive protein (CRP) was 7.5 mg/L. The median time between US and MRI was 1 day. Overall, US revealed vasculitic changes more frequently than MRI (p < 0.001). US detected vascular changes in 37% of vessels compared to 21% with MRI. Among patients with chronic disease, US detected vascular changes in 23% of vessels compared to 7% with MRI in (p < 0.001). The same was true for patients with new-onset disease. US detected vasculitic changes in 22% of vessels and MRI detected disease in 6% (p = 0.0004). Compared to contrast-enhanced MRA, US was more sensitive in detecting vasculitic changes in the large arteries, including the axillary, carotid, and subclavian arteries. CONCLUSION: US more frequently detects vasculitic changes in the large arteries compared to contrast-enhanced MRA. When evaluating the cranial vessels, US performs similarly to MRI. This data supports the recommendation that US be considered as a first-line evaluation in patients suspected to have GCA.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.346
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2020
Admission routes1
Has abstractyes

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