Coronary, Carotid, and Lower-extremity Atherosclerosis and Their Interrelationship in Danish Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: Atherosclerosis is highly prevalent among patients with systemic lupus erythematosus (SLE), but has been demonstrated predominantly in non-European SLE cohorts and few investigations have included more than 1 imaging modality. We aimed to investigate the prevalence of atherosclerosis in 3 frequently affected vascular territories, the coronary, carotid, and lower-extremity arteries, in a Danish, predominantly population-based SLE cohort. METHODS: Patients with SLE without prior cardiovascular disease (CVD; n = 103) were screened for coronary artery calcification, carotid intima-media thickening and plaque, and abnormal ankle-brachial index by means of cardiac computed tomography, ultrasound of the carotid arteries, and ankle systolic blood pressure. RESULTS: In patients with SLE, the prevalence of atherosclerosis in any vascular territory was 41%. The distribution of the atherosclerotic manifestations showed an overlap with 45% of the patients having involvement in more than 1 vascular territory. However, more than one-third of the patients with SLE with coronary, carotid, or lower-extremity atherosclerosis exclusively demonstrated this particular manifestation. Based on a multiple logistic regression model, age (p < 0.001), current smoking (p = 0.009), and the Systemic Lupus International Collaborating Clinics (SLICC; p = 0.008) were significant independent risk factors for atherosclerosis at any vascular territory. CONCLUSION: Atherosclerosis is highly prevalent among Danish patients with SLE without prior CVD. Screening for atherosclerosis in 1 vascular territory is insufficient in diagnosing atherosclerosis in patients with SLE. In Danish patients with SLE, the presence of atherosclerosis was not only assigned to traditional CV risk factors, but also associated with SLICC.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".