Myocardial blood flow quantification for evaluation of coronary artery disease by computed tomography
Bibliographic record
Abstract
During the last decade coronary computed tomography angiography (CTA) has become the preeminent non-invasive imaging modality to detect coronary artery disease (CAD) with high accuracy. However, CTA has a limited value in assessing the hemodynamic significance of a given stenosis due to a modest specificity and positive predictive value. In recent years, different CT techniques for detecting myocardial ischemia have emerged, such as CT-derived fractional flow reserve (FFR-CT), transluminal attenuation gradient (TAG), and myocardial CT perfusion (CTP) imaging. Myocardial CTP imaging can be performed with a single static scan during first pass of the contrast agent, with monoenergetic or dual-energy acquisition, or as a dynamic, time-resolved scan during stress by using coronary vasodilator agents (adenosine, dipyridamole, or regadenoson). A number of CTP techniques are available, which can assess myocardial perfusion in both a qualitative, semi-quantitative or quantitative manner. Once used primarily as research tools, these modalities are increasingly being used in routine clinical practice. All these techniques offer the substantial advantage of combining anatomical and functional evaluation of flow-limiting coronary stenosis in the same examination that would be beneficial for clinical decision-making. This review focuses on the state-of the-art and future trends of these evolving imaging modalities in the field of cardiology for the physiologic assessments of CAD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".