Pattern of Arterial Calcification in Patients with Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To evaluate the prevalence and pattern of arterial calcification in patients with asymptomatic systemic lupus erythematosus (SLE) compared with control subjects. SLE patients are prone to adverse cardiovascular events; however, the underlying atherosclerotic process is unknown. Multidetector computed tomography (MDCT) measured arterial calcium score (CS) reflecting underlying atherosclerosis and is closely associated with cardiovascular events. METHODS: Fifty age and sex matched SLE patients and controls were enrolled. All subjects underwent 64 slice MDCT scan to evaluate CS in coronary, carotid arteries and the aorta. RESULTS: As compared with controls, SLE patients had higher mean CS and prevalence of CS > 0 across all vascular beds. After adjustment for age and sex, SLE patient odds of having CS > 0 in any vascular bed was 33.6 (95% CI: 9.5-165.2) were higher versus patients in the control group, mainly due to more prevalent coronary calcification (OR 30.0, 95% CI: 6.7-203.8). In SLE patients, the most frequent vessel with CS > 0 was coronary (42%) followed by carotid artery (24%). Further, arterial calcification occurred early involving 40% of SLE patients at age < 40 years, with increasing prevalence as age advanced. CONCLUSION: Our study confirms that patients with SLE have significantly higher prevalence and extent of systemic arterial calcification compared with age and sex matched controls.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".