Cardiovascular Outcomes Associated with Lowering Low-density Lipoprotein Cholesterol in Rheumatoid Arthritis and Matched Nonrheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To examine the associations between lowering low-density lipoprotein cholesterol (LDL-C) and cardiovascular (CV) outcomes among patients with rheumatoid arthritis (RA) and patients without it. METHODS: Adult patients with RA and 2 age- and sex-matched control cohorts [RA plus general controls (RA/GN), RA plus osteoarthritis (OA) controls (RA/OA)] were identified between January 1, 2007, and December 31, 2011. Patients with a diagnosis of hyperlipidemia who initiated statin therapy without prior CV events were included. Multivariable Cox proportional hazard analyses were used. RESULTS: The study identified 1522 patients with RA with 6511 general controls (RA/GN cohort); and 1746 patients with RA with 2554 OA controls (RA/OA cohort). During followup, mean (SD) LDL-C (mg/dl) was 96.8 (32.7) for RA, 100.1 (35.1) for general controls, and 99.1 (34.3) for OA. The relationship between lowering LDL-C and CV outcomes was similar for both RA and non-RA controls (p for interaction = 0.852 in RA/GN cohort, and p = 0.610 in RA/OA cohort). After adjusting for baseline CV risk factors, lowering LDL-C was associated with a 29%-50% lower risk of CV events (HR [95% CI] = 0.71 [0.57-0.89] in RA/GN, 0.50 [0.43-0.58] in RA/OA). Subgroup analyses showed that lowering LDL-C was associated with a similar degree of reduction of CV events in RA and non-RA controls (HR of 0.67-0.68 for RA, 0.72 for general controls, 0.76 for OA controls). CONCLUSION: Lowering LDL-C levels was associated with reduced CV events. The relationship between lowering LDL-C and CV outcomes in RA was similar to the relationship found in matched general and OA controls.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".