The correlation between carotid artery atherosclerosis and clinical ischemic heart disease in lupus patients
Bibliographic record
Abstract
AIM: The extent of subclinical atherosclerosis can be assessed by ultrasound measurement of carotid intima-media thickness (cIMT) and total plaque area (TPA). We aimed to investigate the correlation between measures of atherosclerosis as documented on imaging studies of the carotid vasculature and clinical coronary artery disease (CAD) in systemic lupus erythematosus (SLE). METHODS: The study patients were recruited from the University of Toronto prospective cohort of SLE patients. Patients who had a history of CAD were compared to those without CAD. TPA and cIMT were measured using high-resolution optimized ultrasound systems. Logistic regression models were used to investigate the strength of association between ultrasound measures of atherosclerosis and CAD. The strength of association as expressed by odds ratio (OR) was compared between TPA and cIMT. RESULTS: A total of 103 SLE patients were analyzed (27 patients with a history of CAD). Carotid IMT correlated only moderately with TPA (r = 0.43, p < 0.001). Both measures were significantly associated with the presence of CAD. However, TPA showed a stronger association than cIMT (OR 9.55 vs. 2.02, respectively). TPA was also more strongly associated with dyslipidemia and hypertension compared to cIMT. CONCLUSIONS: In SLE patients, cIMT correlates only moderately with TPA, suggesting that they measure different phenotypes of atherosclerosis. Carotid TPA correlated better than cIMT with cardiovascular risk factors and CAD, suggesting that it may serve as a better tool for the investigation of atherosclerosis in 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 | 0.004 |
| 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 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".