Combined Orbital and Cranial Vessel Wall Magnetic Resonance Imaging for the Assessment of Disease Activity in Giant Cell Arteritis
Bibliographic record
Abstract
OBJECTIVE: Acute visual impairment is the most feared complication of giant cell arteritis (GCA) but is challenging to predict. Magnetic resonance imaging (MRI) evaluates orbital pathology not visualized by an ophthalmologic examination. This study combined orbital and cranial vessel wall MRI to assess both orbital and cranial disease activity in patients with GCA, including patients without visual symptoms. METHODS: Patients with suspected active GCA who underwent orbital and cranial vessel wall MRI were included. In 14 patients, repeat imaging over 12 months assessed sensitivity to change. Clinical diagnosis of ocular or nonocular GCA was determined by a rheumatologist and/or ophthalmologist. A radiologist masked to clinical data scored MRI enhancement of structures. RESULTS: Sixty-four patients with suspected GCA were included: 25 (39%) received a clinical diagnosis of GCA, including 12 (19%) with ocular GCA. Orbital MRI enhancement was observed in 83% of patients with ocular GCA, 38% of patients with nonocular GCA, and 5% of patients with non-GCA. MRI had strong diagnostic performance for both any GCA and ocular GCA. Combining MRI with a funduscopic examination reached 100% sensitivity for ocular GCA. MRI enhancement significantly decreased after treatment (P < 0.01). CONCLUSION: In GCA, MRI is a sensitive tool that comprehensively evaluates multiple cranial structures, including the orbits, which are the most concerning site of pathology. Orbital enhancement in patients without visual symptoms suggests that MRI may detect at-risk subclinical ocular disease in GCA. MRI scores decreased following treatment, suggesting scores reflect inflammation. Future studies are needed to determine if MRI can identify patients at low risk for blindness who may receive less glucocorticoid therapy.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".