Assessment of Coronary Risk Based on Cumulative Exposure to Lipids in Systemic Lupus Erythematosus
Bibliographic record
Abstract
OBJECTIVE: To quantify the independent role of each of low-density lipoprotein cholesterol (LDL-C), total cholesterol:high-density lipoprotein cholesterol ratio (TC:HDL-C), triglyceride (TG) level, and HDL-C as a marker of coronary risk in systemic lupus erythematosus (SLE). METHODS: Patients with lipid measurements taken before a coronary event (or last clinic visit) were included. Mean and time-adjusted mean (TAM) levels were calculated for each lipid variable in each patient. Time-dependent proportional hazards regression models were used to quantify the risk of coronary event [myocardial infarction (MI) or angina], after adjustment for age. RESULTS: Among 384 patients, over a mean (SD) followup of 3.81 (2.58) years, there were 21 "first" coronary events (6 MI, 15 angina). Mean and TAM LDL-C (HR 1.83, 95% CI 1.19-2.81, p = 0.006), TC:HDL ratio (HR 1.43, 95% CI 1.02-2.00, p = 0.04), and TG (HR 2.11, 95% CI 1.32-3.39, p = 0.0019) were predictive of coronary event at subsequent visits. In contingency table analysis, TAM LDL-C cutpoint of 2.0 mmol/l had a sensitivity and negative predictive value for coronary event of 85.7% (95% CI 63.7-97.0) and 93.9% (95% CI 83.1-98.7), respectively. However, at this cutpoint the specificity was only 12.7% (95% CI 9.4-16.5). CONCLUSION: This study links LDL-C, TC:HDL-C ratio, and TG to coronary risk in patients with SLE and quantifies the magnitude of this risk. SLE-specific risk assessment levels for lipids may be selected to optimize positive or negative predictive values.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.001 | 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".