The Evolving View of Coronary Artery Calcium: A Personalized Shared Decision-Making Tool in Primary Prevention
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
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.
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 | 0.017 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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