Present treatment options for unstable angina and non-Q-wave myocardial infarction
Bibliographic record
Abstract
Unstable angina is one of the commonest life‐threatening medical emergencies. Despite enormous advances in the understanding of the pathophysiology of unstable angina, development of newer drugs and modern intervention techniques, there are considerable controversies about its most effective and definitive management. We review the literature to produce an evidence‐based practical guide to a uniform approach to managing unstable angina, including non‐Q‐wave myocardial infarction. Annually, there are likely to be at least 130 000 cases of unstable angina (UA) in the UK, 130 000 in France, 188 000 in Germany, 129 000 in Italy and 80 000 in Spain.1 In 1991, it constituted about 55% of all coronary care unit (CCU) admissions.2 It may result in death or non‐fatal myocardial infarction in up to 20% within 30 days of an ischaemic event.3 Among 9146 patients of UA treated in the Duke University Medical Centre between 1985 and 1992, the highest risk of death was found within the first 48 h.4 Because of the uncertain outcome, patients of UA have priority over the stable myocardial infarction (MI) for admission in a CCU bed. As the clinical differentiation between UA and non‐Q‐wave myocardial infarction (NQMI) is usually difficult initially, the two conditions are usually treated in a similar way, under the term ‘acute coronary syndrome‘.4,,5 Despite its common prevalence and high morbidity and mortality, the treatment strategy of UA is not clearly defined. We address the controversies and present the treatment options currently available for UA/NQMI. The presence of one or more of the following may define UA:1,,4 (i) angina at rest; (ii) angina that increases in severity or duration or frequency; (iii) new‐onset angina at least Canadian Cardiovascular Society Grade III severity. Non‐Q‐wave MI may be defined as an increase in cardiac enzymes in the absence of Q‐wave …
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".