CT-Derived Fractional Flow Reserve (FFR <sub>CT</sub> ): From Gatekeeping to Roadmapping
Bibliographic record
Abstract
Coronary computed tomography angiography (CCTA) has emerged as the preferred modality in the diagnosis of coronary artery disease, but it is limited by modest specificity. By applying principles of computational fluid dynamics, flow fraction reserve, a measure of lesion-specific ischemia that is used to guide revascularization, can be noninvasively derived from CCTA, the so-called computed tomography–derived flow fractional reserve (FFR CT ). The accuracy of FFR CT in discriminating ischemia has been extensively validated, and it has been shown to improve the specificity of CCTA. Compared to other stress myocardial perfusion imaging, FFR CT has superior or comparable accuracy. Clinical studies have provided strong evidence that FFR CT has significant prognostic implications and informs clinical decisions for revascularization, serving as a gatekeeper to invasive coronary angiography. In addition, FFR CT-based tools can be used to simulate the physiological consequences of different revascularization strategies, thus providing the roadmap to achieve complete revascularization. Although challenges remain, ongoing research and randomized controlled trials are expected to address current limitations and better define its role in clinical practice.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 teacher head, 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".