High‐Resolution Magnetic Resonance Imaging of Scalp Arteries for the Diagnosis of Giant Cell Arteritis: Results of a Prospective Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To examine the concordance between high-resolution magnetic resonance imaging (MRI) of the scalp arteries and temporal artery biopsy for the diagnosis of giant cell arteritis (GCA). METHODS: We conducted a prospective cohort study of patients with suspected GCA. Participants underwent high-field 3T MRI of the scalp arteries followed by temporal artery biopsy. Arterial wall thickness and enhancement on multiplanar postcontrast T1-weighted spin-echo images were graded according to a published severity scale (range 0-3). MRI findings were compared with temporal artery biopsy results and the American College of Rheumatology (ACR) criteria for GCA. RESULTS: One hundred seventy-one patients were included in the study. Temporal artery biopsy findings were positive in 31 patients (18.1%), and MRI findings were abnormal in 60 patients (35.1%). ACR criteria were met in 137 patients (80.1%). With temporal artery biopsy as the reference test, MRI had a sensitivity of 93.6% (95% confidence interval [95% CI] 78.6-99.2) and a specificity of 77.9% (95% CI 70.1-84.4). The corresponding negative predictive value of MRI was 98.2% (95% CI 93.6-99.8) and positive predictive value was 48.3% (95% CI 35.2-61.6). CONCLUSION: In patients with suspected GCA, normal findings on scalp artery MRI are very strongly associated with negative temporal artery biopsy findings. This suggests that MRI could be used as the initial diagnostic procedure in GCA, with temporal artery biopsy being reserved for patients with abnormal MRI findings.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".