Coronary CT Angiography Guided Medical Therapy in Subclinical Atherosclerosis
Bibliographic record
Abstract
The goals of primary prevention in coronary atherosclerosis are to avoid sudden cardiac death, myocardial infarction or the need for revascularization procedures. Successful prevention will rely on accurate identification, effective therapy and monitoring of those at risk. Identification and potential monitoring can be achieved using cardiac computed tomography (CT). Cardiac CT can determine coronary artery calcification (CAC), a useful surrogate of coronary atherosclerosis burden. Cardiac CT can also assess coronary CT angiography (CCTA). CCTA can identify arterial lumen narrowing and highlight mural atherosclerosis hitherto hidden from other anatomical approaches. Herein we consider the role of CCTA and CAC-scoring in subclinical atherosclerosis. We explore the use of these modalities in screening and discuss data that has used CCTA for guiding primary prevention. We examine therapeutic trials using CCTA to determine the effects of plaque-modifying therapies. Finally, we address the role of CCTA and CAC to guide therapy as defined in current primary prevention documents. CCTA has emerged as an essential tool in the detection and management of clinical coronary artery disease. To date, its role in subclinical atherosclerosis is less well defined, yet with modern CT scanners and continued pharmacotherapy development, CCTA is likely to achieve a more prominent place in the primary prevention of coronary atherosclerosis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".