Stroke frequency, associated factors, and clinical features in primary systemic vasculitis: a multicentric observational study
Bibliographic record
Abstract
OBJECTIVES: The cerebral vessels may be affected in primary systemic vasculitis (PSV), but little is known about cerebrovascular events (CVEs) in this population. This study aimed to determine the frequency of CVEs at the time of diagnosis of PSV, to identify factors associated with CVEs in PSV, and to explore features and outcomes of stroke in patients with PSV. METHODS: Data from adults newly diagnosed with PSV within the Diagnostic and Classification Criteria in VASculitis (DCVAS) study were analysed. Demographics, risk factors for vascular disease, and clinical features were compared between patients with PSV with and without CVE. Stroke subtypes and cumulative incidence of recurrent CVE during a prospective 6-month follow-up were also assessed. RESULTS: The analysis included 4828 PSV patients, and a CVE was reported in 169 (3.50%, 95% CI 3.00-4.06): 102 (2.13% 95% CI 1.73-2.56) with stroke and 81 (1.68% 95% CI 1.33-2.08) with transient ischemic attack (TIA). The frequency of CVE was highest in Behçet's disease (9.5%, 95% CI 5.79-14.37), polyarteritis nodosa (6.2%, 95% CI 3.25-10.61), and Takayasu's arteritis (6.0%, 95% CI 4.30-8.19), and lowest in microscopic polyangiitis (2.2%, 95% CI 1.09-3.86), granulomatosis with polyangiitis (2.0%, 95% CI 1.20-3.01), cryoglobulinaemic vasculitis (1.9%, 95% CI 0.05-9.89), and IgA-vasculitis (Henoch-Schönlein) (0.4%, 95% CI 0.01-2.05). PSV patients had a 11.9% cumulative incidence of recurrent CVE during a 6-month follow-up period. CONCLUSION: CVEs affect a significant proportion of patients at time of PSV diagnosis, and the frequency varies widely among different vasculitis, being higher in Behçet's. Overall, CVE in PSV is not explained by traditional vascular risk factors and has a high risk of CVE recurrence.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".