Association Between Major Adverse Cardiovascular Events and the Gensini Score or Coronary Artery Calcification Score in Hypertensive Patients Who Have Undergone Coronary Computed Tomography Angiography
Bibliographic record
Abstract
Background: From the Fukuoka University Coronary Computed Tomography Angiography (FU-CCTA) registry, we present major adverse cardiovascular events (MACEs) in hypertensive patients who have undergone CCTA, and the association between MACEs and the Gensini score of coronary arteries or the coronary artery calcification (CAC) score. Methods: Of the patients who underwent CCTA for coronary artery disease (CAD) screening at Fukuoka University Hospital, 318 hypertensive patients who had at least one cardiovascular risk factor or suspected CAD were enrolled. The patients were divided into two groups: MACEs and non-MACEs groups. The severity of atherosclerosis of coronary arteries was assessed by the Gensini score. The CAC score was also defined by computed tomography (CT) images at the time of CCTA. A primary endpoint was MACEs (all-cause death, ischemic stroke, acute myocardial infarction, coronary revascularization). The patients were followed for up to 5 years. Results: The patients were 68 ± 10 years, and 50% were males. The percentages of smoking, dyslipidemia, diabetes, and chronic kidney disease were 39%, 70%, 26% and 37%, respectively. The %males, %smoking, CAC score and Gensini score in the MACEs group were significantly higher than those in the non-MACEs group. On the other hand, the differences in age, dyslipidemia, diabetes, or chronic kidney disease between the groups were not seen. A multivariate analysis was performed regarding the presence or absence of MACE by logistic regression analysis of the CAC score or Gensini score in addition to conventional risk factors as independent variables. A Cox regression analysis revealed significant relationships for both the CAC score (P = 0.043) and the Gensini score (P = 0.008). Conclusions: The CAC score and the Gensini score could predict MACEs in hypertensive patients who have undergone CCTA.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".