Abstract 16715: Anatomical Data Defined by 64-Slice CT Angiography Predicts Prognosis of Coronary Artery by-Pass Patients More Accurately That Clinical Risk Predictors
Bibliographic record
Abstract
Objective: We sought to determine the incremental prognostic value of 64 multi-slice coronary computed tomography angiography (CCTA) in coronary artery bypass (CABG) patients. Background: Prognostication in CABG patients can be difficult. Anatomical assessment of native coronary artery disease and graft patency may provide useful information, but the utility of CCTA in the assessment of CABG patients is unknown. Methods: 657 CABG patients with all cause mortality follow up were identified from a multicenter CCTA registry, of 10,628 patients from 5 CCTA centres. Clinical risk was profiled with modified logistic and additive EuroSCOREs. CCTA defined coronary anatomy. Patients were classified by unprotected coronary territory (UCT), or a summary of native vessel disease and graft patency: the coronary artery protection score (CAPS). Results: 76.6% of patients were male and the median age was 68 years. 44 deaths occurred over 48 months follow-up. LVEF, creatinine, age, severity of native vessel disease, UCT, CAPS and EuroSCOREs were univariate predictors of mortality (p<0.001). In multivariate analysis using additive EuroSCORE, UCT (p=0.004) and CAPS were predictive of events (p<0.001). In comparison to additive EuroSCORE, CAPS score was associated with a 27% net reclassification index. Conclusions: CCTA provides incremental anatomical data to clinical risk assessment to better determine the prognosis of symptomatic patients post CABG. CAPS evaluation using CCTA may help determine those patients at highest risk.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.006 | 0.001 |
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".