Adenosine triphosphate stress dual-source computed tomography to identify myocardial ischemia: comparison with invasive coronary angiography
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to investigate the utility incremental diagnostic value of combined assessment with coronary CT angiography (CCTA) and myocardial CT perfusion imaging (CTP) using dual-energy technology with an Adenosine Triphosphate (ATP) load technique. MATERIALS AND METHODS: Twenty-one patients underwent ATP-provocation dual-energy CT and CAG. We compared the diagnostic accuracy with CAG, for ischemic region due coronary stenosis by CCTA alone and CCTA combined with CTP (Combined CCTA/CTP). RESULTS: All of 21 patients CTP images could be evaluated, however 8 CCTA images could not be evaluated by calcification and motion artifact, so assessability was 61.9% (13/21) for CCTA alone, and 100% for Combined CCTA/CTP. With CAG results as a comparison, the sensitivity, specificity, positive predictive value, and negative predictive value were, respectively, 83.3% (20/24), 74.4% (29/39), 66.7% (20/30), and 87.8% (29/33) for CCTA alone, and 66.7% (16/24), 92.3% (36/39), 84.2% (16/19), and 81.8% (36/44) for combined CCTA/CTP. The diagnostic accuracy of the two methods were 77.8% (49/63) and 82.5% (52/63). CONCLUSION: Dual-energy CT may be a useful modality for perfusion assessment and correlated well with the severity of stenosis on CAG. This technique may even be of use in cases of severe calcification in the coronary artery wall.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".