Longterm Outcomes and Treatment After Myocardial Infarction in Patients with Rheumatoid Arthritis
Bibliographic record
Abstract
OBJECTIVE: To investigate the risk profiles, treatment, and outcomes of patients with rheumatoid arthritis (RA) with myocardial infarction (MI) and matched MI patients without RA. METHODS: We used a population-based cohort of Olmsted County, Minnesota, residents with MI from the period 1979-2009. We identified 77 patients who fulfilled the American College of Rheumatology 1987 criteria for RA and 154 MI patients without RA matched for age, sex, and calendar year. Data collection from medical records included RA and MI characteristics, antirheumatic and cardioprotective medications, reperfusion therapy, and outcomes (mortality, heart failure, and recurrent ischemia). RESULTS: The mean age at MI was 72.4 years and 55% of patients were female in both cohorts. Cardiovascular risk factor profiles, MI characteristics, and treatment with reperfusion therapy or cardioprotective medications were similar in MI patients with and those without RA. Patients with RA experienced poorer longterm outcomes compared to patients without RA--for mortality: hazard ratio (HR) 1.47; 95% CI 1.04, 2.08; and for recurrent ischemia: HR 1.51; 95% CI 1.04, 2.18. CONCLUSION: MI patients with RA received similar treatment with reperfusion therapy and cardioprotective medications and had similar short-term outcomes compared to patients without RA. Patients with RA had poorer longterm outcomes. Despite similar treatment, MI patients with RA had worse longterm outcomes than MI patients without RA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".