The transluminal attenuation gradient in coronary CT angiography
Bibliographic record
Abstract
OBJECTIVE: Results of the use of the transluminal attenuation gradient (TAG) at coronary CT angiography (CCTA) to predict hemodynamically significant disease vary widely. This study tested whether diagnostic performance of TAG to predict fractional flow reserve (FFR) ≤ 0.8 is improved when applied separately to subsets of coronary arteries that carry similar physiological flow. METHODS: 28 patients with 64 × 0.5 mm CCTA and invasive FFR in ≥1 major coronary artery were retrospectively evaluated. Two readers assessed TAG in each artery. The receiver operating characteristic (ROC) area under the curve (AUC) was used to assess the diagnostic performance of TAG to detect hemodynamically significant disease following a clinical use rule [negative: FFR > 0.8 or ≤ 25% diameter stenosis (DS) at invasive catheter angiography; positive: FFR ≤ 0.8 or ≥ 90% DS at invasive catheter angiography]. ROC AUC was compared for all arteries pooled together, vs separately for arteries carrying similar physiological flow (Group 1: all left anterior descending plus right-dominant left circumflex; Group 2: right-dominant RCA plus left/co-dominant left circumflex). RESULTS: Of the 84 arteries, 30 had FFR measurements, 30 had ≤25% DS and 13 had ≥90% DS. 11 arteries with 26-89% DS and no FFR measurement were excluded. TAG interobserver reproducibility was excellent (Pearson r = 0.954, Bland-Altman bias: 0.224 Hounsfield unit cm). ROC AUC to detect hemodynamically significant disease was higher when considering arteries separately (Group 1 AUC = 0.841, p = 0.039; Group 2 AUC = 0.840, p = 0.188), than when pooling all arteries together (AUC = 0.661). CONCLUSION: Incorporating information on the physiology of coronary flow via the particular vessel interrogated and coronary dominance may improve the accuracy of TAG, a simple measurement that can be quickly performed at the time of CCTA interpretation to detect hemodynamically significant stenosis in individual coronary arteries. Advances in knowledge: The interpretation of TAG may benefit by incorporating information regarding which coronary artery is being interrogated.
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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 | 0.003 | 0.014 |
| 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 | 0.000 | 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 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".