Atherosclerotic vascular events in a multinational inception cohort of systemic lupus erythematosus
Bibliographic record
Abstract
OBJECTIVE: To describe vascular events during an 8-year followup in a multicenter systemic lupus erythematosus (SLE) inception cohort and their attribution to atherosclerosis. METHODS: Clinical data, including comorbidities, were recorded yearly. Vascular events were recorded and attributed to atherosclerosis or not. All of the events met standard clinical criteria. Factors associated with atherosclerotic vascular events were analyzed using descriptive statistics, t-tests, and chi-square tests. Stepwise multivariate logistic regression was used to assess the association of factors with vascular events attributed to atherosclerosis. RESULTS: Since 2000, 1,249 patients have been entered into the cohort. There have been 97 vascular events in 72 patients, including: myocardial infarction (n = 13), angina (n = 15), congestive heart failure (n = 24), peripheral vascular disease (n = 8), transient ischemic attack (n = 13), stroke (n = 23), and pacemaker insertion (n = 1). Fifty of the events were attributed to active lupus, 31 events in 22 patients were attributed to atherosclerosis, and 16 events were attributed to other causes. The mean +/- SD time from diagnosis to the first atherosclerotic event was 2.0 +/- 1.5 years. Compared with patients followed for 2 years without atherosclerotic events (n = 615), at enrollment, patients with atherosclerotic vascular events were more frequently white, men, older at diagnosis of SLE, obese, smokers, hypertensive, and had a family history of coronary artery disease. On multivariate analysis, only male sex and older age at diagnosis were associated factors. CONCLUSION: In an inception cohort with SLE followed for up to 8 years, there were 97 vascular events, but only 31 were attributable to atherosclerosis. Patients with atherosclerotic events were more likely to be men and to be older at diagnosis 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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".