Using Magnetic Resonance Angiography to Measure Abnormal Synovial Blood Vessels in Early Inflammatory Arthritis: A New Imaging Biomarker?
Bibliographic record
Abstract
Objective. To ascertain whether magnetic resonance angiography (MRA) can reliably detect synovial neovascularization in subjects with early inflammatory arthritis. Methods. Subjects with 6 weeks to 6 months of clinical evidence of inflammatory hand arthritis had a radiograph, power Doppler ultrasound (PDU) scan, magnetic resonance imaging (MRI), and contrast enhanced MRA performed on the more symptomatic hand. Ultrasound examination of the wrist and 2nd–5th metacarpophalangeal (MCP) joints was scored for erosions, synovial thickening, and synovial blood flow. MRI were assessed using the OMERACT Rheumatoid Arthritis MRI Score (RAMRIS). MRA was used to assess the number of abnormal vessels in the 2nd–5th MCP and in the wrist. Results. Of 30 subjects, 66.7% showed abnormal vasculature on MRA in the MCP and/or wrist; mean number of abnormal vessels was 5.24 (range 0–22). Number of abnormal vessels on MRA was strongly correlated with degree of blood flow seen in the corresponding area on PDU (r = 0.79, p ≤ 0.0001). The number of abnormal vessels was highly correlated with MRI MCP synovitis scores (r = 0.69, p ≤ 0.0001), MRI wrist synovitis scores (r = 0.73, p ≤ 0.0001), and ultrasound synovitis scores (r = 0.68, p ≤ 0.0001). Conclusion. In this cross-sectional pilot study, MRA identified abnormal vessels in patients with early inflammatory arthritis. This is the first report of MRA visualizing abnormal vessels in this patient population. If the degree of neovascularization in early inflammatory arthritis predicts disease course, MRA evidence of abnormal vessels could be a new imaging biomarker.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".