Impact of Zero Coronary Artery Calcium Scoring on Downstream Cardiac Testing and Cardiac Outcomes Compared With No Testing
Bibliographic record
Abstract
Background: The impact of coronary artery calcium (CAC) scoring on downstream resource utilisation and outcomes remains unclear, especially in those with zero CAC. Methods: Consecutive CAC scores from two academic hospitals in Toronto, Ontario, were linked to population-based databases. Subjects with zero CAC without previous cardiovascular disease were propensity score matched with a non-CAC-tested control group for age, sex, cardiovascular risk factors, and comorbidities. Downstream cardiac testing, acute myocardial infarction, heart failure (HF) hospitalisations, and HF emergency department (ED) visits were compared between the 2 groups. Results: A total of 4884 patients underwent CAC scoring, of whom 2709 had zero CAC (mean 52.9 ± 10.6 years), 55.4% women. At 3.4 years, graded-stress testing (hazard ratio [HR] 1.24, 95% confidence interval [95% CI] 1.14-1.35), stress echocardiography (HR 1.80, 95% CI 1.59-2.05), and cardiac magnetic resonance imaging (HR 3.40, 95% CI 2.55-4.53) use was higher in the zero CAC group, whereas myocardial perfusion scintigraphy (HR 1.08, 95% CI 0.97-1.21) and catheterisation (HR 1.14, 95% CI 0.91-1.44) were similar and percutaneous coronary intervention (HR 0.58, 95% CI 0.35-0.98) and coronary artery bypass grafting (HR 0.14, 95% CI 0.03-0.61) were lower. There was an approximately 5-fold lower rate of myocardial infarction (HR 0.22, 95% CI 0.10-0.51) in the zero CAC group and no difference in HF hospitalisations (HR 1.15, CI 95% 0.53-2.48) or ED admissions (HR 1.21, 95% CI 0.58-2.52). Conclusions: Our results support the utility of zero CAC in limiting interventional cardiovascular procedures while maintaining an association with reduced downstream cardiovascular events.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".