Cerebrovascular Events in Systemic Lupus Erythematosus: Results From an International Inception Cohort Study
Bibliographic record
Abstract
OBJECTIVE: To determine the frequency, characteristics, and outcomes of cerebrovascular events (CerVEs), as well as clinical and autoantibody associations in a multiethnic/racial inception cohort of patients with systemic lupus erythematosus (SLE). METHODS: A total of 1,826 patients were assessed annually for 19 neuropsychiatric (NP) events, including 5 types of CerVEs: 1) stroke, 2) transient ischemia, 3) chronic multifocal ischemia, 4) subarachnoid/intracranial hemorrhage, and 5) sinus thrombosis. Global disease activity (Systemic Lupus Erythematosus Disease [SLE] Activity Index 2000), damage scores (SLE International Collaborating Clinics/American College of Rheumatology Damage Index), and Short Form 36 (SF-36) scores were collected. Time to event, linear and logistic regressions, and multistate models were used as appropriate. RESULTS: CerVEs were the fourth most frequent NP event: 82 of 1,826 patients had 109 events; of these events, 103 were attributed to SLE, and 44 were identified at the time of enrollment. The predominant events were stroke (60 of 109 patients) and transient ischemia (28 of 109 patients). CerVEs were associated with other NP events attributed to SLE, non-SLE-attributed NP events, African ancestry (at US SLICC sites), and increased organ damage scores. Lupus anticoagulant increased the risk of first stroke and sinus thrombosis and transient ischemic attack. Physician assessment indicated resolution or improvement in the majority of patients, but patients reported sustained reduction in SF-36 summary and subscale scores following a CerVE. CONCLUSION: CerVEs, the fourth most frequent NP event in SLE, are usually attributable to lupus. In contrast to good physician-reported outcomes, patients reported a sustained reduction in health-related quality of life following a CerVE.
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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 | 0.002 |
| 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 | 0.001 |
| 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".