4054Betablockers and outcomes in stable coronary artery disease. Insights from the CLARIFY registry
Bibliographic record
Abstract
Background: The role of beta blockers (BB) in the management of stable coronary artery disease (CAD) remains disputed. Data suggesting benefit are largely derived from post myocardial infarction trials antedating the advent of revascularization. Recent studies suggest that BB may have limited benefit in stable CAD patients without heart failure (HF). Purpose: To describe the use of BB and their association with outcomes in a large contemporary cohort of stable CAD patients. Methods: CLARIFY is an observational longitudinal cohort of stable CAD patients from 45 countries enrolled in 2009–2010. The inclusion criteria were any of the following (non-mutually exclusive): prior myocardial infarction (MI); angiographic coronary stenosis >50%; proven symptomatic myocardial ischemia; or prior revascularization procedure. The main exclusion criteria were severe diseases including advanced HF or conditions interfering with life expectancy. Follow-up was by yearly visits up to 5 years. Comparisons were done with multivariable adjusted Cox proportional hazards models.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".