Clinical Features and Radiological Findings in Large Vessel Vasculitis: Are Takayasu Arteritis and Giant Cell Arteritis 2 Different Diseases or a Single Entity?
Bibliographic record
Abstract
OBJECTIVE: Takayasu arteritis (TAK) and giant cell arteritis (GCA) are 2 major variants of large vessel vasculitis (LVV). The frequent involvement of large vessels in GCA has raised the possibility that TAK and GCA should be regarded as 1 disease. By detailed phenotyping of a single-center cohort, we aimed to define the differences between TAK and GCA. METHODS: Forty-five patients (23 TAK, 22 GCA) were identified. Baseline characteristics, clinical symptoms, laboratory data, enhanced computed tomography/magnetic resonance imaging, treatments, and clinical courses were retrospectively assessed with descriptive statistics. In addition, latent class analysis of the 45 patients was performed to explore phenotypic differences. RESULTS: Patients with GCA had more frequent headache (p < 0.01), higher C-reactive protein levels (p = 0.01), and higher erythrocyte sedimentation rates (p = 0.03) than did patients with TAK at diagnosis. With the exception of subdiaphragmatic lesions, the distributions of vessel lesions were not different between TAK and GCA. However, focusing on subclavian and carotid arteries, long tapered-type stenotic lesions were more frequent in GCA than in TAK (p < 0.01). The proportion of patients without relapse was higher in GCA (60%) than in TAK (22%, p = 0.01). Latent class analysis also divided patients with LVV into 2 separate groups consistent with TAK and GCA. CONCLUSION: The differences observed in clinical symptoms, inflammatory markers, radiological findings, and clinical courses suggested that TAK and GCA were 2 different diseases. Latent class analysis supported these results. The shape of stenotic lesions in the subclavian and carotid arteries is a useful discriminator between TAK and GCA.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".