Coronary artery calcium burden across the pooled cohort equation versus the American Heart Association PREVENT risk calculator
Bibliographic record
Abstract
Background: The Pooled Cohort Equation (PCE) and the Predicting Risk of cardiovascular disease EVENTs (PREVENT) calculator assess atherosclerotic cardiovascular disease (ASCVD) risk. Coronary artery calcium (CAC) scoring enhances ASCVD risk stratification beyond traditional risk factors, but CAC burden across PCE versus PREVENT risk groups remains unclear. This study evaluates the distribution of CAC burden across PCE and PREVENT risk groups. Methods: A retrospective cohort study was conducted involving 7610 asymptomatic patients who underwent clinically indicated CAC scoring between 2010 and 2023. Ten-year ASCVD risk was calculated using PCE and PREVENT (low<5, borderline-intermediate 5-19, high≥20 %). CAC scores (0, 1-99, 100-299, ≥300) were compared across risk groups using Kendall Tau tests. Results: < 0.001). For borderline-intermediate risk, 71 % for PCE and 79 % for PREVENT had CAC>0, while for low risk, 45 % and 51 %. Within the borderline risk group (5-7.5 %), 20.7 % of PCE and 35 % of ASCVD PREVENT group had CAC>100. Conclusions: A significant number of patients had non-zero CAC in low-risk stratification groups for both PREVENT and PCE. CAC distribution was heterogeneous in the borderline-intermediate groups for both PREVENT and PCE. These results emphasize the significance of CAC in further stratifying risk beyond the PCE and PREVENT.
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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 | 0.015 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".