A Coronary Artery Calcium Score of Zero in Patients Who Have Undergone Coronary Computed Tomography Angiography Is Associated With Freedom From Major Adverse Cardiovascular Events
Bibliographic record
Abstract
BACKGROUND: The coronary artery calcification score (CACS) is a good marker of future cardiovascular risk. We determined the association between the CACS and the prognosis in patients who have undergone coronary computed tomography angiography (CCTA). METHODS: We performed a prospective cohort study and enrolled 502 consecutive patients who underwent CCTA for screening of coronary artery disease (CAD) at Fukuoka University Hospital (FU-CCTA Registry) and either were clinically suspected of having CAD or had at least one cardiovascular risk factor with a follow-up of up to 5 years. The patients were divided into CACS = 0 and CACS > 0 groups. Using CCTA, ≥ 50% coronary stenosis was diagnosed as CAD, and the number of significantly stenosed coronary vessels (VD), Gensini score and CACS were quantified. The primary endpoint was major adverse cardiovascular events (MACE: cardiovascular death, ischemic stroke, acute myocardial infarction and coronary revascularization). RESULTS: %CAD, the number of VD and the Gensini score in the CACS = 0 group were significantly lower than those in the CACS > 0 group. %MACE in the CACS = 0 group was also significantly lower than that in the CACS > 0 group. Kaplan-Meier curves indicated that the CACS = 0 group showed significantly greater freedom from MACE than the CACS > 0 group (P = 0.008). Finally, only CACS = 0 was independently associated with MACE (odd ratio: 0.41, 95% confidence interval: 0.17 - 0.97, P = 0.041). CONCLUSIONS: A CACS of 0 in patients who underwent CCTA was associated with a good prognosis.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".