The Evolving Field of Acute Coronary Syndrome Management: A Critical Appraisal of the 2023 European Society of Cardiology Guidelines for the Management of Acute Coronary Syndrome
Bibliographic record
Abstract
Acute coronary syndromes (ACS), encompassing conditions like ST-elevation myocardial infarction (STEMI) and non-ST-elevation acute coronary syndromes (NSTE-ACS), represent a significant challenge in cardiovascular care due to their complex pathophysiology and substantial impact on morbidity and mortality. The 2023 European Society of Cardiology (ESC) guidelines for ACS management introduce several updates in key areas such as invasive treatment timing in NSTE-ACS, pre-treatment strategies, approaches to multivessel disease, and the use of imaging modalities including computed tomography (CT) coronary angiography, magnetic resonance imaging (MRI), and intracoronary imaging techniques, such as optical coherence tomography (OCT) and intravascular ultrasound (IVUS). They also address a modulation of antiplatelet therapy, taking into consideration different patient risk profiles, and introduce new recommendations for low-dose colchicine. These guidelines provide important evidence-based updates in practice, reflecting an evolution in the understanding and management of ACS, yet some potentially missed opportunities for more personalized care and technology adoption are discussed.
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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 | 0.025 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".