Antirheumatic drug use and the risk of acute myocardial infarction
Bibliographic record
Abstract
OBJECTIVE: To assess the risk of acute myocardial infarction (AMI) associated with the use of disease-modifying antirheumatic drugs (DMARDs) and other medications commonly used in rheumatoid arthritis (RA). METHODS: We conducted a nested case-control analysis within a cohort of subjects with RA, observed between 1999 and 2003, identified from the PharMetrics claims database. For each first AMI hospitalization identified during followup, 10 controls matched on sex, age, and time of study entry were randomly selected from the cohort. Conditional logistic regression was used to estimate the rate ratio (RR) of AMI associated with the current use of anti-RA therapy, as measured from dispensed prescriptions, after adjustment for AMI risk factors. RESULTS: The cohort included 107,908 subjects (average age 54 years at cohort entry). During followup, 558 AMI cases occurred (3.4 per 1,000 per year). AMI rate was significantly decreased with the current use of any DMARD (adjusted RR 0.80, 95% confidence interval [95% CI] 0.65-0.98). This effect was consistent across all DMARDs, including methotrexate (RR 0.81, 95% CI 0.60-1.08), leflunomide (RR 0.28, 95% CI 0.12-0.65), and other traditional DMARDs (RR 0.67, 95% CI 0.46-0.97), but not biologic agents (RR 1.30, 95% CI 0.92-1.83). AMI rate increased with the use of glucocorticoids (RR 1.32, 95% CI 1.02-1.72) but not with nonselective nonsteroidal antiinflammatory drugs (RR 1.05, 95% CI 0.81-1.36) or cyclooxygenase 2 (COX-2) inhibitors (RR 1.11, 95% CI 0.87-1.43). CONCLUSION: DMARD use is associated with a reduction in AMI risk in patients with RA. No risk increase was found with the COX-2 inhibitors in this population.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".