Atherosclerotic Vascular Events in Systemic Lupus Erythematosus: An Evolving Story
Bibliographic record
Abstract
OBJECTIVE: Atherosclerotic vascular events (AVE) are a major cause of mortality and morbidity in systemic lupus erythematosus (SLE). We aimed to determine the effect of early recognition and therapy for both classic risk factors for AVE and for SLE, on the burden of AVE in SLE in recent decades. METHODS: Inception patients who entered the University of Toronto Lupus Clinic between 1975 and 1987 followed to 1992 (Cohort 1), and between 1999 and 2011 followed to 2016 (Cohort 2) were studied. AVE attributed to atherosclerosis and occurring during the 17 years were identified. SLE disease activity and therapy as well as hypertension, hypercholesterolemia, hyperglycemia, and smoking were assessed. Analysis included descriptive statistics on baseline characteristics, traditional risk factors over the followup, outcome rates by each 100 person-years (PY), Kaplan-Meier cumulative AVE curves, as well as competing risk Cox models adjusted by inverse probability weights. RESULTS: Of the 234 patients in Cohort 1, 26 patients (11%) had an AVE compared with 10 of 262 patients (3.8%) in Cohort 2. The rate per 100 PY of followup was 1.8 in Cohort 1 and 0.44 in Cohort 2 (p < 0.0001). Better control of all risk factors and disease activity was achieved in Cohort 2. There was a reduction of 60% in the risk for AVE in Cohort 2. CONCLUSION: The incidence of AVE in SLE in the modern era has declined in large part owing to more effective management of classic coronary artery risk factors and of SLE.
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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 | 0.009 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.011 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.008 |
| 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".