The Evolving View of Coronary Artery Calcium: A Personalized Shared Decision-Making Tool in Primary Prevention
Bibliographic record
Abstract
The 2018 American Heart Association and American College of Cardiology (AHA/ACC) cholesterol management guideline considers current evidence on coronary artery calcium (CAC) testing while incorporating learnings from previous guidelines. More than any previous guideline update, this set encourages CAC testing to facilitate shared decision making and to individualize treatment plans. An important novelty is further separation of risk groups. Specifically, the current prevention guideline recommends CAC testing for primary atherosclerotic cardiovascular disease (ASCVD) prevention among asymptomatic patients in borderline and intermediate risk groups (5-7.5% and 7.5-20% 10-year ASCVD risk). This additional sub-classification reflects the uncertainty of treatment strategies for patients broadly considered to be "intermediate risk," as treatment recommendations for high and low risk groups are well established. The 2018 guidelines, for the first time, clearly recognize the significance of a CAC score of zero, where intensive statin therapy is likely not beneficial and not routinely recommended in selected patients. Lifestyle modification should be the focus in patients with CAC = 0. In this article, we review the recent AHA/ACC cholesterol management guideline and contextualize the transition of CAC testing to a guideline-endorsed decision aid for borderline-to-intermediate risk patients who seek more definitive risk assessment as part of a clinician-patient discussion. CAC testing can reduce low-value treatment and focus primary prevention therapy on those most likely to benefit.
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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 | 0.009 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".