Ability of non-fasting and fasting triglycerides to predict coronary artery disease in lupus patients
Bibliographic record
Abstract
OBJECTIVES: To test whether non-fasting and fasting triglyceride (TG) levels differ in individual patients and whether TG (non-fasting and fasting) levels predict coronary artery disease (CAD) in lupus patients. METHODS: Using predefined criteria for a patient's inclusion in this study, we identified the first available set of non-fasting and fasting TG measurements on each individual lupus patient seen in the clinic since 1996. We dichotomized TG values as normal/abnormal and determined whether non-fasting and fasting TG levels differ in each individual patient. We determined whether TG levels (non-fasting and fasting) predict CAD in all consecutive lupus patients seen in the clinic since 1973 using time-dependent time-to-event analysis and stepwise reduction analysis. RESULTS: Part 1: 514 patients were identified. The time between first non-fasting and fasting TG measurements available was 3.2 months. Examining dichotomized TG values as normal/abnormal, there was concordance between fasting and non-fasting TG in 92% of the visits. Non-fasting TG levels were 0.16 (0.75) higher than fasting TG levels (P < 0.001). Part 2: among 1289 patients, 638 had at least one elevated TG level and the length of follow-up from the first TG level recorded to CAD or last clinic visit was 8.82 years. One hundred and four patients developed CAD. TG (non-fasting and fasting) levels predicted CAD with a hazard ratio of 1.15 (95% CI 1.02, 1.29). CONCLUSIONS: Although non-fasting TG levels were statistically higher than the fasting TG levels, the clinical significance of this difference is uncertain. TG (non-fasting and fasting) levels can predict CAD in lupus 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".