Concordance of Time-of-Flight MRA and Digital Subtraction Angiography in Adult Primary Central Nervous System Vasculitis
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: 3D-TOF-MRA and DSA are 2 available tools to demonstrate neurovascular involvement in primary central nervous system vasculitis. We aimed to compare the diagnostic concordance of vessel imaging using 3D-TOF-MRA and DSA in patients with primary central nervous system vasculitis. MATERIALS AND METHODS: We retrospectively identified all patients included in the French primary central nervous system vasculitis cohort of 85 patients who underwent, at baseline, both intracranial 3D-TOF-MRA and DSA in an interval of no more than 2 weeks and before treatment initiation. Two neuroradiologists independently reviewed all 3D-TOF-MRA and DSA imaging. Brain vasculature was divided into 25 arterial segments. Concordance between 3D-TOF-MRA and DSA for the identification of arterial stenosis was assessed by the Cohen κ Index. RESULTS: Thirty-one patients met the inclusion criteria, including 20 imaged with a 1.5T MR unit and 11 with a 3T MR unit. Among the 25 patients (81%) with abnormal DSA findings, 24 demonstrated abnormal 3D-TOF-MRA findings, whereas all 6 remaining patients with normal DSA findings had normal 3D-TOF-MRA findings. In the per-segment analysis, concordance between 1.5T 3D-TOF-MRA and DSA was 0.82 (95% CI, 0.75-0.93), and between 3T 3D-TOF-MRA and DSA, it was 0.87 (95% CI, 0.78-0.91). CONCLUSIONS: 3D-TOF-MRA shows a high concordance with DSA in diagnostic performance when analyzing brain vasculature in patients with primary central nervous system vasculitis. In patients with negative 3T 3D-TOF-MRA findings, the added diagnostic value of DSA is limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".