Abstract 111: The Prevalence and Management of Angina Among Stable CAD Patients in Outpatient Cardiology Practices in the United States: Insights From the Angina Prevalence and Provider Evaluation of Angina Relief (APPEAR) Study.
Bibliographic record
Abstract
Background: Eliminating angina is a primary goal in the management of chronic coronary artery disease (CAD). There are few data quantifying the prevalence, severity and intensity of angina treatment in contemporary cardiology practice in the United States. Methods: Leveraging the ACC PINNACLE registry, we conducted a cross-sectional study across 23 US outpatient cardiology clinics to examine the burden and management of angina in patients with stable CAD. Angina was assessed using the Seattle Angina Questionnaire (SAQ) angina frequency (AF) domain score and categorized as daily/weekly (SAQ AF < or = 60), monthly (score 61-99), and no (score =100) angina. At each site, we examined the proportion of patients with daily/weekly (frequent) angina and the proportion of patients with frequent angina who were treated with optimal medical treatment (> 2 anti-anginal medications). Results: Among 1154 patients from 23 sites, 8.0% (n=93) reported daily/weekly angina, 24.3% (n=280) monthly angina, and 67.7% (n=781) no angina. The proportion of patients with frequent angina at each site ranged from 2.0-24.0%. Among these patients, 53.8% (n=50) were on optimal medical treatment, with wide variability noted across sites (0%-100%; Figure). Conclusion: Nearly a third of CAD outpatients followed by cardiologists report angina, with 8.0% having frequent symptoms. Among frequent angina patients, just over half were on optimal medical management with wide variability across sites, suggesting important opportunities to improve care in chronic CAD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".