P3625Barriers to the use and titration of betablockers in patients with stable coronary artery disease. Insights from the CLARIFY registry
Bibliographic record
Abstract
Background: Betablockers (BB) are often used in patients with coronary artery disease (CAD). They are known to be associated with adverse effects that might prevent use, lead to discontinuation or prevent titration to full dose. Purpose: To describe the use, dosing and tolerability of BB in a large contemporary registry of stable CAD. Methods: CLARIFY is an observational longitudinal cohort of contemporary stable CAD patients from 45 countries enrolled in 2009–2010. The main exclusion criteria were all conditions including advanced HF interfering with life expectancy. BB type, dose, contraindications (CI) and side effects were prospectively collected. Results: At baseline among 32376 patients, 7765 (24.0%) were not treated with BB, of whom only 2700 (34.8%) had a prior history of intolerance or CI to BB (table): mainly obstructive pulmonary disease exacerbation or fatigue. By 5 years, 939 (12.1%) patients without BB started using them. Among the 32376 participants, 24611 (76.0%) received BB. The most frequently used agents, representing 94.4% of the total, were bisoprolol (34.3%), metoprolol (28.0%), atenolol (14.8%), carvedilol (11.6%) and nebivolol (5.7%). Among patients receiving any of these 5 agents 44.2% received ≤ half of the target dose and 14.1% had the full recommended dose. Among patients receiving BB, 2018 (8.2%) had prior symptoms indicating intolerance or CI, mainly fatigue or bradycardia. In 11.5% this led to BB discontinuation and in 70.2% to dose reduction. By 5 years, 8.3% of patients with BB at baseline stopped using BB, and 4.3% reduced the dose because of side effects.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| 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 | 0.001 |
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".