Diagnosing and treating stable angina. a contemporary approach for practicing physicians
Bibliographic record
Abstract
Longer life expectancy and advancements in coronary artery disease management have improved life expectancy and survival, increasing the prevalence of chronic coronary syndromes (CCS). Angina is a common symptom in patients with CCS but remains underdiagnosed and undertreated. Contemporary guidelines provide detailed information on diagnosing and treating angina based on evidence and expert consensus; however, their extensive nature may hinder uptake by non-specialists. This review presents a practical approach to diagnosing stable angina, followed by the three pillars of CCS management: 1) healthy lifestyle including appropriate exercise, diet, and avoiding toxic habits; 2) optimal medical therapy, including treatment recommended to prevent cardiovascular events and drugs for the control of myocardial ischemia and angina tailored to the patient’s comorbidities; and 3) myocardial revascularization when indicated. This approach may be useful for practicing physicians but is not intended to substitute more detailed and authoritative documents. Checklists are proposed to help focus patient–physician interactions and make follow-up visits more efficient. This approach seeks to increase the proportion of correct angina diagnoses and patients receiving evidence-based treatments, emphasizing the importance of patient education, managing residual angina, and reducing cardiovascular risk. We include reference to the recently published 2024 ESC guidelines on chronic coronary syndromes. Advances in the diagnosis and treatment of coronary heart diseases have greatly increased survival after a heart attack and increased life expectancy. As a result, an increasing number of people are living with chronic heart diseases that impair blood flow to the heart muscle, causing chest pain (angina). This can limit one’s quality of life and capacity to perform daily functions without pain. There are several underlying causes of angina, and it also occurs in people who have not had a heart attack. Management should address three important areas: (i) Lifestyle changes should include regular exercise, a heart-healthy diet, and avoidance of smoking, while keeping blood pressure, serum cholesterol levels, and other so-called risk factors under control to prevent future cardiovascular events. (ii) Anti-angina medications (antianginal drugs) should be personalized based on the underlying cause of a patient’s angina, their cardiovascular characteristics, the presence of other medical conditions and the medications that they are currently taking. The physician may need to adjust this tailored treatment to achieve optimal results for the individual patient. (iii) If this optimal medical therapy does not provide sustained relief, the physician may refer the patient to a cardiology center for further testing and to evaluate whether a revascularization procedure would be appropriate. Administering the optimal antianginal treatment for each patient, and careful adherence to lifestyle recommendations provide the best chance for restoring quality of life, reducing medical visits and improving long-term outcomes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".