P4591Outcomes of stable coronary artery disease worldwide. Insights from the CLARIFY registry
Bibliographic record
Abstract
Background: The epidemiology and management of stable coronary artery disease (CAD) have substantially changed with the advent of revascularization, evidence-based secondary prevention therapies and improved survival following acute coronary syndromes (ACS). While in the last century, stable CAD largely referred to patients with angina pectoris, the spectrum of stable angina is nowadays broader, encompassing long term survivors of ACS, and patients with or without: angina, documented ischemia, history of revascularization or documented angiographic CAD. There are few data describing this broad group of patients. Purpose: To describe epidemiology, contemporary management and long term outcomes of this broad group of patients. Methods: CLARIFY is an observational longitudinal registry. Stable CAD patients from 45 countries were enrolled between 2009–2010. The inclusion criteria were any of: previous myocardial infarction (MI); angiographic evidence of coronary stenosis >50%; documented symptomatic myocardial ischemia; or prior revascularization. The main exclusion criteria were serious non-cardiovascular or other cardiovascular (CV) disease (including advanced heart failure); conditions interfering with life expectancy. Follow-up was by yearly visits up to 5 years. Hazard ratios (HRs) and 95% confidence intervals (CI) were estimated with multivariable adjusted Cox proportional hazards models.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".