Patterns of Arterial Disease in Takayasu Arteritis and Giant Cell Arteritis
Bibliographic record
Abstract
Objective To identify and validate, using computer‐driven methods, patterns of arterial disease in Takayasu arteritis (TAK) and giant cell arteritis (GCA). Methods Patients with TAK or GCA were studied from the Diagnostic and Classification Criteria for Vasculitis (DCVAS) cohort and a combined North American cohort. Case inclusion required evidence of large‐vessel involvement, defined as stenosis, occlusion, or aneurysm by angiography/ultrasonography, or increased 18 F‐fluorodeoxyglucose (FDG) uptake by positron emission tomography (PET) in at least 1 of 11 specified arterial territories. K‐means cluster analysis identified groups of patients based on the pattern of arterial involvement. Cluster groups were identified in the DCVAS cohort and independently validated in the North American cohort. Results A total of 1,068 patients were included (DCVAS cohort: TAK = 461, GCA = 217; North American cohort: TAK = 225, GCA = 165). Six distinct clusters of patients were identified in DCVAS and validated in the North American cohort. Patients with TAK were more likely to have disease in the abdominal vasculature, bilateral disease of the subclavian and carotid arteries, or focal disease limited to the left subclavian artery than GCA ( P < 0.01). Patients with GCA were more likely to have diffuse disease, involvement of bilateral axillary/subclavian arteries, or minimal disease without a definable pattern than TAK ( P < 0.01). Patients with TAK were more likely to have damage by angiography, and patients with GCA were more likely to have arterial FDG uptake by PET without associated vascular damage. Conclusion Arterial patterns of disease highlight both shared and divergent vascular patterns between TAK and GCA and should be incorporated into classification criteria for large‐vessel vasculitis.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| 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".