Bibliographic record
Abstract
The burden of coronary artery disease (CAD) remains high across Europe and the rest of the world. CAD continues to be the main cause of death and a major cause of morbidity and loss of quality of life. The decline in age-standardized mortality rates and in incidence of CAD in many countries illustrates the potential for prevention of premature deaths and for prolonging life expectancy. New therapeutic options for prevention and treatment of CAD have resulted in an increasing number of patients who survive a cardiovascular event; in developed countries the burden has shifted from the middle-aged to the elderly and the prevalence of CAD increases exponentially with aging. CAD is a leading public health problem accounting for a significant proportion of total societal costs and representing 27% of total cardiovascular disease costs. Together with cerebrovascular diseases, CAD accounts for 64% of all cardiovascular deaths. There are a number of lifestyle changes that can be implemented to improve the prognosis of patients with stable CAD, including smoking cessation, adoption of a Mediterranean diet, body weight reduction, and increased physical activity. Concomitant risk factors such as diabetes, dyslipidaemia, and hypertension should be managed aggressively. Current treatment options for stable CAD involve a two-pronged approach combining antianginal treatment to improve symptoms and quality of life along with a cardioprotective treatment to prevent cardiovascular events. Optimal medical treatment should be the initial management approach in the majority of patients with stable CAD, even if extensive and multi-vessel atherosclerosis is involved. A large body of evidence suggests that high resting heart rate (HR) is a potential risk factor for mortality and morbidity in various populations, including patients with CAD. Experimental evidence indicates that high HR plays a role in endothelial dysfunction and atherosclerosis progression. An HR ≥70 b.p.m. is associated with an increased cardiovascular risk. Ongoing randomized trials are evaluating the role of selective HR reduction in improving cardiovascular outcomes. These trial data will be complemented by CLARIFY, a large-scale international registry of outpatients with stable CAD which will analyse not only the baseline characteristics and management practices but will also capture all suspected important determinants of outcomes including resting HR.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.127 | 0.096 |
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".