Coronary Artery Calcium, All-Cause Mortality, and Sudden Cardiac Death in Asymptomatic Populations
Bibliographic record
Abstract
The prognostic value of coronary artery calcium (CAC) assessed by computed tomography (CT) for mortality risk has been discussed in the literature for over 3 decades.While this assessment has advanced through technological improvements and innovative methodologies, its fundamental evaluation framework has remained largely unchanged, and the original scoring system continues to underpin most CT-based calcium measurements.Accordingly, the purpose of the current review was to assess the existing knowledge regarding CAC evaluation and its relationship with all-cause mortality and sudden cardiac death.Overall, the data clearly demonstrate that CAC is a robust independent predictor of mortality.The evidence also suggests some degree of population heterogeneity, as well as a possible bimodal distribution in death prediction based on age.The data support the view that high CAC is strongly associated with an increased risk of all-cause mortality among asymptomatic younger (< 40 years) and older (> 70 years) adults.In contrast, evidence for mortality prediction based on sex remains inconclusive.The number of calcified blood vessels also influences mortality risk, with significantly higher risk in individuals with calcification of 3 or 4 vessels compared to those with 1.A high CAC score presents a substantially greater mortality risk, and scores > 1,000 appear to increase mortality by approximately 10-fold compared to a CAC score of 0. Furthermore, in asymptomatic individuals, CAC assessment has been shown to be a stronger predictor of mortality than traditional cardiovascular risk scoring.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".