Associations Between Methotrexate Use and the Risk of Cardiovascular Events in Patients with Elderly-onset Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: We evaluated the associations between time-varying methotrexate (MTX) use and risk of cardiovascular events (CVE) in patients with rheumatoid arthritis (RA). METHODS: We studied an inception cohort of 23,994 patients with RA diagnosed after their 65th birthday. Multivariable Cox regression models were fit to evaluate the associations between time-varying MTX use, controlling for other risk factors, and time to CVE. Alternative models assessed the cumulative duration of MTX use over the (1) first year, (2) previous year (recent use), and (3) entire duration of followup. We also assessed whether the strength of the association varied over time. RESULTS: Over 115,453 patient-years (PY), 3294 (13.7%) patients experienced a CVE (28.5 events per 1000 PY; 95% CI 27.6-29.5). In the multivariable analyses, the model assessing time-varying continuous use in the most recent year yielded the best fit. Increasing recent MTX use was associated with lower CVE risks (HR 0.79 for continuous use vs no use in past 12 months, 95% CI 0.70-0.88; p < 0.0001). Greater MTX use in the first year after cohort entry was also protective (HR 0.84, 95% CI 0.72-0.96; p = 0.0048), but this effect decreased with increasing followup. In contrast, longer MTX use during the entire followup was not clearly associated with CVE risk (HR 0.98, 95% CI 0.95-1.01; p = 0.1441). CONCLUSION: We observed about a 20% decrease in CVE associated with recent continuous MTX use. Greater MTX use in the first year of cohort entry also appeared to be important in the association between MTX and CVE risk.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".