Premature Aortic Atherosclerosis in Systemic Lupus Erythematosus: A Controlled Transesophageal Echocardiographic Study
Bibliographic record
Abstract
OBJECTIVE: Premature carotid and coronary atherosclerosis are common in systemic lupus erythematosus (SLE), but data on aortic atherosclerosis (AA) are limited. Thus, using multiplane transesophageal echocardiography (TEE), we sought to determine the prevalence and clinical correlates of AA in patients with SLE. METHODS: Forty-seven patients with SLE (44 women, age 38 +/- 12 years) and 21 healthy controls (19 women, age 34 +/- 12 years) underwent clinical and laboratory evaluations and TEE to assess AA defined as aortic intima media thickness (IMT) > 0.86 mm or plaques as > 50% focal IMT as compared with surrounding walls. TEE studies were interpreted by an experienced observer unaware of subjects' clinical data. RESULTS: The prevalence of abnormal aortic IMT, plaques, or both lesions was higher in patients as compared to controls (37%, 23%, and 43% vs 14%, 0%, and 14%, respectively, all p </= 0.02). In patients, age at diagnosis of SLE was the only positive independent predictor of AA [OR 1.12 per year from diagnosis of SLE, 95% confidence interval (CI) 1.04-1.19, p = 0.001] and cyclophosphamide therapy was the only negative independent predictor of AA (OR 0.186, 95% CI 0.153-0.95, p = 0.04, equivalent to 5.4 times less likely to develop AA). CONCLUSION: AA is common in young patients with SLE and is predicted by a later age at diagnosis of SLE, but is negatively correlated with cyclophosphamide therapy. Thus, early diagnosis and more aggressive immunosuppressive therapy may be required to decrease the development and progression of atherosclerosis in patients with 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".