Coronary artery calcium paradox and physical activity
Bibliographic record
Abstract
Reducing the risk of plaque rupture events in individuals without a prior myocardial infarction is an imprecise science. To help clarify whether there is evidence of coronary artery disease and avoid ‘medicalisation’ of otherwise healthy individuals, international guidelines recommend incorporating the measurement of coronary artery calcium alongside risk prediction models.1 Coronary artery calcium serves as a surrogate marker of advanced calcified atherosclerosis and can be calculated from a non-contrast ECG-gated CT scan where a score of 1–99 Agatston units represents subclinical atherosclerosis, and a score of 100 or more Agatston units is considered an appropriate threshold for initiating medical therapy.1 At ≥100 Agatston units, the burden of advanced calcified atherosclerosis justifies statin implementation and this has been validated in a real-world cohort study of 16 996 subjects with a 10-year number needed to treat to prevent one cardiovascular event of 12.2 Many clinicians have advocated the benefits of coronary artery calcium in redefining the cardiovascular risk assessment of healthy individuals, as there is a strong link between high burdens of coronary artery calcium, accelerated progression of calcified plaque and the risk of future myocardial infarction. However, if the burden of calcified plaque is an accurate barometer of cardiovascular risk, one would expect an intervention which reduces an individual’s cardiovascular risk to attenuate progression of calcified plaque. And herein lies the coronary artery calcium paradox; both invasive and non-invasive imaging studies have …
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.016 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".