Magnetic resonance imaging compared to ultrasonography in giant cell arteritis: a cross-sectional study
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".