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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.018 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it