083. COMPARISON OF ARTERIAL PATTERNS OF DISEASE IN TAKAYASU’S ARTERITIS AND GIANT CELL ARTERITIS
Bibliographic record
Abstract
Background: Current classification criteria differentiate between Takayasu’s arteritis (TAK) and giant cell arteritis (GCA) based primarily on clinical assessment, yet patients with TAK and GCA may differ in patterns of arterial disease. This study aimed to use computer-based algorithms to determine if patterns of arterial disease are useful to differentiate TAK from GCA with large-vessel involvement (LV- GCA). Methods: Patients with TAK or LV-GCA were studied from the international, Diagnostic and Classification Criteria for Vasculitis (DCVAS) cohort and a combined North America (NA) cohort (Vasculitis Clinical Research Consortium, National Institutes of Health, and Cleveland Clinic). Case inclusion required evidence of large-vessel involvement, defined as stenosis, occlusion, or aneurysm by imaging or catheter-based angiography or ultrasound, or increased FDG uptake by positron-emission tomography (PET) in at least one of 11 specified arterial territories. K-means cluster analysis was performed to identify clusters of patients based on pattern of arterial involvement. Cluster groups were identified in the DCVAS cohort and independently validated in the combined NA cohort. Results: A total of 1,069 were included (DCVAS: TAK=462, GCA=217; NA: TAK=225, GCA=165). Patients with TAK underwent angiography (95%), ultrasonography (28%), or PET imaging (14%). Patients with LV-GCA underwent angiography (50%), ultrasonography (52%), and/or PET imaging (58%). Six distinct clusters of patients were identified in DCVAS and validated in the NA cohort (Figure). Patients in Clusters One, Two, and Three were significantly more likely to have TAK, and patients in Cluster Six were significantly more likely to have LV-GCA. Patients in Clusters Four and Five were equally likely to have TAK or LV-GCA, but assignment in these clusters was driven largely by stenotic disease for TAK and FDG-uptake without stenosis for GCA. Out of all study patients, involvement of the abdominal aorta and renal/mesenteric arteries was the most specific pattern for TAK (134 TAK vs 11 GCA, p < 0.01), while bilateral subclavian/axillary disease was the most specific pattern for GCA (92 GCA vs 23 TAK, p < 0.01). Conclusion: These findings support the incorporation of arterial patterns of disease into classification criteria for large-vessel vasculitis and highlight shared and divergent vascular phenotypes between TAK and GCA. Disclosures: This study was supported by the Intramural Research Program at the National Institute of Arthritis and Musculoskeletal and Skin Diseases. The Vasculitis Clinical Research Consortium (VCRC) is part of the Rare Diseases Clinical Research Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), National Center for Advancing Translational Science (NCATS). The VCRC is funded through collaboration between NCATS, and the National Institute of Arthritis and Musculoskeletal and Skin Diseases (U54 AR057319) and also received funding from the National Center for Research Resources (U54 RR019497).DCVAS is funded by the American College of Rheumatology, European League Against Rheumatism, and the Vasculitis Foundation.
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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) | 0.000 | 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) | 0.003 | 0.001 |
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".