Contemporary rationale for non-invasive imaging of adverse coronary plaque features to identify the vulnerable patient: a Position Paper from the European Society of Cardiology Working Group on Atherosclerosis and Vascular Biology and the European Association of Cardiovascular Imaging
Bibliographic record
Abstract
Atherosclerotic plaques prone to rupture may cause acute myocardial infarction (MI) but can also heal without causing an event. Certain common histopathological features, including inflammation, a thin fibrous cap, positive remodelling, a large necrotic core, microcalcification, and plaque haemorrhage are commonly found in plaques causing an acute event. Recent advances in imaging techniques have made it possible to detect not only luminal stenosis and overall coronary atherosclerosis burden but also to identify such adverse plaque characteristics. However, the predictive value of identifying individual adverse atherosclerotic plaques for future events has remained poor. In this Position Paper, the relationship between vulnerable plaque imaging and MI is addressed, mainly for non-invasive assessments but also for invasive imaging of adverse plaques in patients undergoing invasive coronary angiography. Dynamic changes in atherosclerotic plaque development and composition may indicate that an adverse plaque phenotype should be considered at the patient level rather than for individual plaques. Imaging of adverse plaque burden throughout the coronary vascular tree, in combination with biomarkers and biomechanical parameters, therefore holds promise for identifying subjects at increased risk of MI and for guiding medical and invasive treatment.
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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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.012 | 0.021 |
| Insufficient payload (model declined to judge) | 0.003 | 0.004 |
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".