Assessment of damage in Takayasu’s arteritis
Bibliographic record
Abstract
OBJECTIVES: To evaluate damage and clinical characteristics associated with damage in Takayasu's arteritis (TAK). METHODS: Patients with TAK enrolled in a multicentre, prospective, observational study underwent standardized damage assessment every 6 months using the Vasculitis Damage Index (VDI) and the Large-Vessel Vasculitis Index of Damage (LVVID). RESULTS: The study included 236 patients with TAK: 92% female, 81% Caucasian; median (25th, 75th percentile) disease duration = 2.6 (0.12, 6.9) years. Eighty-four percent had follow-up: median (25th, 75th) duration 4.1 (1.9, 7.5) years. Items of damage were present in 89% on VDI, 87% on LVVID, in the peripheral vascular (76% VDI, 74% LVVID) and cardiac (40% VDI, 45% LVVID) systems. During follow-up, 42% patients had new damage, including major vessel stenosis/arterial occlusion (8%), limb claudication (6%), hypertension (7%), aortic aneurysm (4%) and bypass surgery (4%). Disease-specific damage accounted for >90% of new items. Older age, relapse and longer duration of follow-up were associated with new damage items; a higher proportion of patients without new damage were on MTX (P <0.05). Among 48 patients diagnosed with TAK within 180 days of enrolment, new damage occurred in 31% on VDI and 52% on LVVID. History of relapse was associated with new damage in the entire cohort while in patients with a recent diagnosis, older age at diagnosis was associated with new damage. CONCLUSION: Damage is present in >80% of patients with TAK even with recent diagnosis and >40% of patients accrue new, mainly disease-specific damage. Therapies for TAK that better control disease activity and prevent damage should be prioritized.
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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 | 0.001 |
| 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".