A combination of anatomical and functional evaluations improves the prediction of cardiac event in patients with coronary artery bypass
Bibliographic record
Abstract
OBJECTIVE: To study the usefulness of combined risk stratification of coronary CT angiography (CTA) and myocardial perfusion imaging (MPI) in patients with previous coronary-artery-bypass grafting (CABG). DESIGN: A retrospective, observational, single centre study. SETTING AND PATIENTS: 204 patients (84.3% men, mean age 68.7±7.6) undergoing CTA and MPI. MAIN OUTCOME MEASURES: CTA defined unprotected coronary territories (UCT; 0, 1, 2 or 3) by evaluating the number of significant stenoses which were defined as the left main trunk ≥50% diameter stenosis, other native vessel stenosis ≥70% or graft stenosis ≥70%. Using a cut-off value with receiver-operating characteristics analysis, all patients were divided into four groups: group A (UCT=0, summed stress score (SSS)<4), group B (UCT≥1, SSS<4), group C (UCT=0, SSS≥4) and group D (UCT≥1, SSS≥4). RESULTS: Cardiac events, as a composite end point including cardiac death, non-fatal myocardial infarction, unstable angina requiring revascularisation and heart-failure hospitalisation, were observed in 27 patients for a median follow-up of 27.5 months. The annual event rates were 1.1%, 2%, 5.7% and 12.9% of patients in groups A, B, C and D, respectively (log rank p value <0.0001). Adding UCT or SSS to a model with significant clinical factors including left ventricular ejection fraction, time since CABG and Euro SCORE II improved the prediction of events, while adding UCT and SSS to the model improved it greatly with increasing C-index, net reclassification improvement and integrated discrimination improvement. CONCLUSIONS: The combination of anatomical and functional evaluations non-invasively enhances the predictive accuracy of cardiac events in patients with CABG.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".