Derivation of an angiographically based classification system in Takayasu’s arteritis: an observational study from India and North America
Bibliographic record
Abstract
OBJECTIVES: To develop and replicate, using data-driven methods, a novel classification system in Takayasu's arteritis based on distribution of arterial lesions. METHODS: Patients were included from four international cohorts at major academic centres: India (Christian Medical College Vellore); North America (National Institutes of Health, Vasculitis Clinical Research Consortium and Cleveland Clinic Foundation). All patients underwent whole-body angiography of the aorta and branch vessels, with categorization of arterial damage (stenosis, occlusion or aneurysm) in 13 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 replicated in the North American cohorts. RESULTS: A total of 806 patients with Takayasu's arteritis from India (n = 581) and North America (n = 225) were included. Three distinct clusters defined by arterial damage were identified in the Indian cohort and replicated in each of the North American cohorts. Patients in cluster one had significantly more disease in the abdominal aorta, renal and mesenteric arteries (P < 0.01). Patients in cluster two had significantly more bilateral disease in the carotid and subclavian arteries (P < 0.01). Compared with clusters one and two, patients in cluster three had asymmetric disease with fewer involved territories (P < 0.01). Demographics, clinical symptoms and clinical outcomes differed by cluster. CONCLUSION: This large study in Takayasu's arteritis identified and replicated three novel subsets of patients based on patterns of arterial damage. Angiographic-based disease classification requires validation by demonstrating potential aetiological or prognostic implications.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".