Effect of antimalarial agents on the fasting lipid profile in systemic lupus erythematosus.
Bibliographic record
Abstract
OBJECTIVE: To ascertain the relative effect of antimalarial (AM) agents on fasting lipid fractions in patients with systemic lupus erythematosus (SLE). METHODS: The study was cross sectional including all patients with SLE who were seen in our lupus clinic with fasting lipid profiles measured as part of evaluation from November 1995 to March 1999. RESULTS: A total of 123 patients with a mean age of 45.3 years and mean disease duration 13.4 years were studied; 73.2% were taking prednisone with a mean +/- SD dose of 10.9 +/- 9.2 mg/day, 48.0% were taking AM, and 30.8% were taking both. In the entire group, patients taking AM had a 12.5% lower total cholesterol (TC) (5.11 +/- 1.27 vs 5.84 +/- 1.23; p = 0.002), 22.1% lower very low density lipid-cholesterol (VLDL-C) (0.66 +/- 0.40 vs 0.85 +/- 0.39; p = 0.01), and 15.7% lower LDL-C (3.01 +/- 1.14 vs 3.58 +/- 1.10; p = 0.007). For patients taking prednisone, those taking concomitant AM (n = 38) had significantly lower TC (5.26 +/- 1.30 vs 5.99 +/- 1.29; p = 0.01), VLDL-C (0.65 +/- 0.39 vs 0.85 +/- 0.41; p = 0.02), and LDL-C (3.05 +/- 1.20 vs 3.69 +/- 1.09; p = 0.01) than those without AM (n = 48). For patients taking < or = 10 mg/day prednisone, TC (4.69 +/- 0.88 vs 5.74 +/- 1.20; p < 0.001), VLDL-C (0.61 +/- 0.37 vs 0.83 +/- 0.44; p = 0.05), and LDL-C (2.57 +/- 0.76 vs 3.49 +/- 1.04; p < 0.001) were still lower in patients with concomitant AM (n = 22) than those without AM (n = 36). CONCLUSION: TC, VLDL-C, and LDL-C levels were significantly lower in patients taking AM, including patients taking concomitant prednisone. Thus AM may have beneficial effects in SLE in addition to disease suppression.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".