Carotid versus coronary atherosclerosis burdens in acute compared with chronic symptomatic coronary artery disease
Bibliographic record
Abstract
Prediction of coronary events remains elusive. Carotid atherosclerosis may be a surrogate for coronary risk, as carotid and coronary diseases occur simultaneously — albeit at times with a weak association — depending on clinical presentation. We investigated carotid and coronary atherosclerosis in men with new-onset unstable coronary artery disease (CAD) presenting with acute ST-segment elevation myocardial infarction (STEMI) vs. long-standing severe chronic stable angina (CSA). Bilateral carotid artery and 3-vessel coronary artery atherosclerosis burdens were measured within 1 month, respectively, by 3D-volumetric carotid magnetic resonance imaging and coronary angiography-derived modified CASS-50 score. Men with STEMI (n = 50) and long-standing CSA (n = 50), matched for age, were enrolled (58.6 ± 8.8 years). All of them had carotid atherosclerosis. Atherosclerosis burden was greater in the carotid arteries of STEMI vs. CSA (wall volume: 196.2 ± 44.4 vs. 169.2 ± 38.0 mm3/4 mm, p = 0.002), but greater in the coronary arteries of CSA vs. STEMI (modified CASS-50 score: 3 vs. 1, p < 0.0001). Normalized wall index (NWI) of internal carotid was associated with modified CASS-50 score in STEMI (ρ = 0.40, p = 0.022) and in CSA (ρ = −0.39, p = 0.031). Carotid atherosclerosis was observed in all CAD patients, and atherosclerosis burden in carotid and in coronary arteries varied according to clinical presentation.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".