Semiquantified Noncalcified Coronary Plaque in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: A major cause of morbidity and mortality in systemic lupus erythematosus (SLE) is accelerated coronary atherosclerosis. New technology (computed tomographic angiography) can measure noncalcified coronary plaque (NCP), which is more prone to rupture. We report on a study of semiquantified NCP in SLE. METHODS: Patients with SLE (n = 147) with no history of cardiovascular disease underwent 64-slice coronary multidetector computed tomography (MDCT). The MDCT scans were evaluated quantitatively by a radiologist, using dedicated software. RESULTS: The group of 147 patients with SLE was 86% female, 70% white, 29% African American, and 3% other ethnicity. The mean age was 51 years. In our univariate analysis, the major traditional cardiovascular risk factors associated with noncalcified plaque were age (p = 0.007), obesity (p = 0.03; measured as body mass index), homocysteine (p = 0.05), and hypertension (p = 0.04). Anticardiolipin (p = 0.026; but not lupus anticoagulant) and anti-dsDNA (p = 0.03) were associated with higher noncalcified plaque. Prednisone and hydroxychloroquine therapy had no effect, but methotrexate (MTX) use was associated with higher noncalcified plaque (p = 0.0001). In the best multivariate model, age, current MTX use, and history of anti-dsDNA remained significant. CONCLUSION: Our results suggest that serologic SLE (anti-dsDNA) and traditional cardiovascular risk factors contribute to semiquantified noncalcified plaque in SLE. The association with MTX is not understood, but should be replicated in larger studies and in multiple centers.
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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 | 0.003 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".