Angina at 1 Year After Myocardial Infarction<subtitle>Prevalence and Associated Findings</subtitle>
Bibliographic record
Abstract
BACKGROUND: Eradication of angina is a primary goal of care after myocardial infarction (MI). However, the prevalence of angina 1 year after MI and factors associated with it are unknown. METHODS: From January 1, 2003, through June 28, 2004, 2498 patients with acute MI were recruited from 19 hospitals in the United States. Among this multicenter cohort of patients, angina was measured by the Seattle Angina Questionnaire 1 year after hospitalization for MI. Multivariate regression modeling identified the sociodemographic factors, clinical history, MI presentation, inpatient treatments, and outpatient treatments associated with 1-year angina, adjusted for site. RESULTS: Of 1957 patients in the cohort, 389 (19.9%) reported angina 1 year after MI. After multivariate analysis, patients with 1-year angina were more likely to be younger (relative risk [RR] per 10-year decrease, 1.19; 95% confidence interval [CI], 1.09-1.30), to be nonwhite males (RR, 1.50; 95% CI, 1.16-1.96), to have had prior angina (RR, 1.78; 95% CI, 1.54-2.06), to have undergone prior coronary artery bypass graft surgery (RR, 1.92; 95% CI, 1.51-2.44), and to experience recurrent rest angina during their hospitalization (RR, 1.54; 95% CI, 1.22-1.93). Among the outpatient variables, patients with 1-year angina were more likely to continue smoking (RR, 1.23; 95% CI, 1.02-1.48), to undergo revascularization after index hospitalization (percutaneous coronary intervention or coronary artery bypass graft) (RR, 1.37; 95% CI, 1.09-1.73), and to have significant new (RR, 1.96; 95% CI, 1.34-2.87), persistent (RR, 1.88; 95% CI, 1.29-2.75), or transient (RR, 1.77; 95% CI, 1.49-2.11) depressive symptoms. CONCLUSIONS: Angina occurs in nearly 1 of 5 patients 1 year after MI. It is associated with several modifiable factors, including persistent smoking and depressive symptoms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".