Bibliographic record
Abstract
The burden of coronary artery disease (CAD) remains high and is the main cause of death and a major cause of morbidity. The treatment of patients with symptomatic CAD is aimed at preventing myocardial infarction and death, and reducing the symptoms of angina and occurrence of myocardial ischaemia. Significant evidence shows that elevated heart rate plays an important role in triggering ischaemic events in patients with CAD and is an important predictor of cardiovascular morbidity and mortality. Ivabradine is a potent anti-anginal agent which works by specifically lowering heart rate. Its antianginal and anti-ischaemic efficacy has been established in a number of randomized placebo-controlled trials, ivabradine being non-inferior to beta-blockers and to calcium antagonists. These trials have also shown that ivabradine is well tolerated and can be safely combined with other cardiovascular agents. The ASSOCIATE study showed that ivabradine provides significant heart rate reduction and improvement in all exercise test criteria in patients with stable angina receiving the beta-blocker atenolol. In addition to its effects on myocardial ischemia and anginal symptoms, ivabradine has the potential to improve clinical outcomes in patients with limiting angina or in patients with a heart rate above 70 b.p.m. as suggested in the BEAUTIFUL trial. In summary, ivabradine is a potent anti-anginal agent, which can be used alone (when beta-blockers are contraindicated or not tolerated) or in combination with beta-blockers, with excellent tolerability.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".