Diagnosis and management of angina for the cardiac nurse
Bibliographic record
Abstract
The burden of treating stable angina is vast, as it is estimated that almost 2 million people currently have or have had angina in the UK ( Health and Social Care Information Centre (HSCIC), 2006 ). Angina is the main symptom of myocardial ischaemia, usually caused by atherosclerotic obstructive coronary artery disease, restricting bloodflow and therefore oxygen delivery to the heart muscle ( National Institute for Health and Care Excellence (NICE), 2011 ). Characteristic features of stable angina include tightness or heaviness across the chest, which can radiate to the left arm, neck, jaw and back. However, it is important to be aware that some people present with atypical symptoms. A diagnosis of stable angina can be based on clinical assessment alone or with the addition of diagnostic testing. Diagnosic tests used in this instance can include: exercise testing, computerised tomography (CT), myocardial perfusion scanning and stress echocardiography, in addition to blood tests and electrocardiogram which all patients will undergo. Management of angina includes medical therapy, the aim of which is to help reduce symptoms and prevent cardiovascular events such as myocardial infarction and stroke ( Montalescot et al, 2013 ). Guidelines on stable angina by NICE (2011) and the European Society of Cardiology (ESC) ( Montalescot et al, 2013 ) provide us with recommendations on which medications should be prescribed. Lifestyle changes are a key aspect of angina management: namely in the areas of diet, exercise, smoking, diabetes, hypertension and psychological issues.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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".