Predictors of lipid abnormalities in children with new-onset systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVE: Lipid abnormalities in patients with systemic lupus erythematosus (SLE) are common and likely are one of the causes of premature atherosclerosis in these patients. Our aims were to determine the frequency and pattern of dyslipoproteinemia at presentation of pediatric SLE; and to determine the association between dyslipoproteinemia and markers of disease activity and inflammatory markers at presentation of pediatric SLE. METHODS: Serum lipid measurements were obtained at diagnosis before corticosteroid treatment for an inception cohort of 54 patients. Total cholesterol, triglyceride, LDL-C, and HDL-C levels were regressed on measures of inflammation, disease activity, and disease symptoms. RESULTS: At least one lipid abnormality was present in the majority of patients (63%), an elevated triglyceride level being the most common lipid abnormality (62%). Triglycerides were best predicted by fibrinogen, nephritis, and pleuritis (model R2 = 0.6). Albumin, C4, and white blood cell count were found to predict HDL-C (model R2 = 0.6). Erythrocyte sedimentation rate, central nervous system involvement, nasal ulcers, and nephritis were found as predictors for LDL-C:HDL-C (model R2 = 0.5). No significant predictors were found for total cholesterol or LDL-C. The European Consensus Lupus Activity Measure disease activity score best predicted abnormal triglyceride and HDL-C levels (OR 1.7, 95% CI 1.2-2.3). CONCLUSION: Children with newly diagnosed SLE exhibited the distinct pattern of dyslipoprotein of increased triglycerides and depressed HDL-C that was twice as common in the presence of kidney disease. This lipid profile puts them at risk for premature atherosclerosis. Good disease control and individualized use of lipid-lowering agents based on the observed pattern of lipid abnormalities may lower the risk of premature atherosclerosis in these patients.
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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) | 0.001 | 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".