Abstract 16606: 30-day and 2-year Prognostic Information of Total Atheroma Volume, Segment Stenosis Score, and Traditional Coronary Artery Stenosis Assessment by CT Angiography - Results From the CORE320 International Study
Bibliographic record
Abstract
Introduction: Among abundant information on coronary artery disease (CAD) features by CT angiography (CTA), total atheroma volume and segment stenosis score (SSS) have recently shown promise for clinical utility. Methods: We followed 379 patients with suspected or known CAD enrolled in the CORE320 study for 2 years after 320-detector row CT coronary angiography. CT images were analyzed for semi-automatically derived total % atheroma volume (total atherosclerotic burden/vessel volume analyzed) and SSS in addition to traditional stenosis assessment (≥50%). Outcome variables were 1) 30-day revascularization and 2) major adverse cardiac events (MACE) after 2 years follow up. Events included cardiac death, myocardial infarction, hospitalization for acute chest pain or heart failure, arrhythmia, and revascularization. Area under the curve (AUC) and Kaplan-Meyer analysis were used to compare risk prediction and survival analysis according to CT CAD characteristics. Results: Thirty-day revascularization was most accurately predicted by CT stenosis assessment (AUC 75, confidence interval [CI] 71-80) vs. % atheroma volume (70 [65-74] and CTA SSS (67 [62-72]) (p=0.007). Prediction of MACE (45 late revascularizations, 5 myocardial infarctions, 1 cardiac death, 8 hospitalizations for chest pain or congestive heart failure, and 1 arrhythmia) was similar for % atheroma volume (64 [71 for patients without history of CAD]) and CTA stenosis assessment (65 [70]) but risk discrimination using common criteria trended favorably for % atheroma volume (FIGURE). Accuracy was low for CTA SSS (58 [62]). Conclusions: Semi-automated assessment of % total atheroma volume by CTA performs similarly to standard stenosis assessment for predicting short and long term event rates, especially revascularization, in patients with suspected CAD and holds promise for more nuanced risk discrimination. In contrast, CTA segment stenosis score performed only modestly in our analysis.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".