084. DISCOVERY AND VALIDATION OF A NOVEL ANGIOGRAPHIC CLASSIFICATION SCHEME IN TAKAYASU’S ARTERITIS
Bibliographic record
Abstract
Background: Takayasu’s arteritis (TAK) is characterized by variable patterns of damage throughout the large arteries. Methods: Data was used from patients with TAK from four independent cohorts: one in India and three in North America (NA). All patients underwent whole-body angiography of the aorta and branch vessels, with categorization of involvement (stenosis, occlusion, or aneurysm) in 13 arterial territories. K-means cluster analysis was performed to identify subgroups of patients based on pattern of angiographic involvement. Cluster groups were identified in the Indian cohort and independently validated in the NA cohorts. Results: 581 and 225 patients with TAK were included from the Indian and NA cohorts, respectively. Three distinct clusters were identified in the Indian cohort and validated in the NA cohorts. Patients in Cluster 1 had significantly more disease in the abdominal aorta, renal, and mesenteric arteries (p < 0.01). Patients in Cluster 2 had significantly more bilateral disease in the carotid and subclavian arteries (p < 0.01). Compared to Clusters 1 and 2, patients in Cluster 3 had asymmetric disease with fewer involved territories (p < 0.01). In the Indian and the NA cohorts, patients in Clusters 1 and 2 compared to Cluster 3 were more likely to have arterial occlusions (58% vs 82% vs 37%; p < 0.01) and a history of tuberculosis (8% vs 10% vs 3%; p = 0.03). Disease onset in childhood (28% vs 16% vs 19%; p < 0.01) and hypertension (71% vs 42% vs 39%; p < 0.01) were more common in Cluster 1. Stroke (0% vs 22% vs 5%; p = 0.03), vision loss (0% vs 33% vs 6%; p = 0.01), carotidynia (3% vs 26% vs 9%; p = 0.01) and persistent disease activity (46% vs 59% vs 44%; p = 0.02) were significantly more prevalent in Cluster 2. Conclusion: This large study in TAK identified and validated three novel subsets of patients based on patterns of arterial disease. Angiographic-based disease classification may help identify causal disease factors and enable stratified clinical decision making in this complex, clinically heterogeneous disease. Disclosures: Intramural NIAMS Program and the VCRC Abstract 084 Table 1: <0.01 Temporal artery examination abnormality defined as cord-like, tender, or absent/diminished pulse. Pulse abnorn1ality defined as reduced or absent pulse. LV involvement defined as presence of stenosis, occlusion, aneurysm, FDG uptake, or halo sign on angiogram (MR, CT, catheter), PET or ultrasound in the aorta or branch arteries (excluding temporal artery).
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".