Non-invasive imaging in coronary syndromes: recommendations of the European Association of Cardiovascular Imaging and the American Society of Echocardiography, in collaboration with the American Society of Nuclear Cardiology, Society of Cardiovascular Computed Tomography, and Society for Cardiovascular Magnetic Resonance
Bibliographic record
Abstract
Coronary artery disease (CAD) is one of the major causes of mortality and morbidity worldwide, with a high socioeconomic impact.(1) Non-invasive imaging modalities play a fundamental role in the evaluation and management of patients with known or suspected CAD. Imaging end-points have served as surrogate markers in many observational studies and randomized clinical trials that evaluated the benefits of specific therapies for CAD.(2) A number of guidelines and recommendations have been published about coronary syndromes by cardiology societies and associations, but have not focused on the excellent opportunities with cardiac imaging. The recent European Society of Cardiology (ESC) 2019 guideline on chronic coronary syndromes (CCS) and 2020 guideline on acute coronary syndromes in patients presenting with non-ST-segment elevation (NSTE-ACS) highlight the importance of non-invasive imaging in the diagnosis, treatment, and risk assessment of the disease.(3)(4) The purpose of the current recommendations is to present the significant role of non-invasive imaging in coronary syndromes in more detail. These recommendations have been developed by the European Association of Cardiovascular Imaging (EACVI) and the American Society of Echocardiography (ASE), in collaboration with the American Society of Nuclear Cardiology, the Society of Cardiovascular Computed Tomography, and the Society for Cardiovascular Magnetic Resonance, all of which have approved the final document.
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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 | 0.012 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.008 | 0.008 |
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".