Soluble E-selectin may predict progression of subclinical atherosclerosis, as measured by coronary artery calcium score and aorta calcium score, in women with systemic lupus erythematosus
Bibliographic record
Abstract
Women with systemic lupus erythematosus (SLE) have increased rates of subclinical atherosclerosis and cardiovascular (CV) events [ 1 , 2 ]. Circulating adhesion molecules (CAMs) have been associated with subclinical atherosclerosis in SLE patients [ 3 , 4 ]. We investigated the significance of CAMs in subclinical atherosclerosis progression, as measured by the coronary artery calcium score (CAC) and aorta calcium score (AS) in women with SLE. Baseline data collected include demographics and circulating adhesion molecule levels. SLE factors collected included modified SLICC/ACR-DI Damage Index (SDI) (excluding CV outcomes). CAC and AS were measured by electron beam or multidimensional computed tomography at baseline and at one follow-up visit in the Study of Lupus Vascular and Bone Long-Term Endpoints (SOLVABLE). High-risk CAC and AS were defined as CAC >10 and AS >100, respectively. Progression in CAC and AS at follow-up was defined as CAC >10 or AS >100 and >10% increase from baseline. Univariate regression models of CAC and AS with risk factors were examined, and further adjusted for age. CAMs measured were ICAM-1, VCAM-1, soluble E-selectin (sESEL), and CD40L. Imaging at baseline and follow-up were performed on 142 subjects; baseline AS scans were not performed in 36 subjects (Table 1 ). Adhesion molecule levels (Table 2 ) and imaging marker progression (Table 3 ) results are presented. In age-adjusted models, only sESEL was significantly associated with AS and CAC progression (Table 4 ). A higher level of sESEL is associated with progression in AS and CAC in women with SLE. While previous studies have shown CAMs association with subclinical atherosclerosis [ 3 , 4 ], these results suggest sESEL may predict progression of subclinical atherosclerosis, as measured by AS and CAC, in women 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 | 0.004 |
| 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 | 0.000 | 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".