Coronary CT Angiography to Guide Percutaneous Coronary Intervention
Bibliographic record
Abstract
Coronary CT angiography (CCTA) has emerged as a powerful noninvasive tool for characterizing the presence, extent, and severity of coronary artery disease (CAD) in patients with stable angina. Recent technological advancements in CT scanner hardware and software have augmented the rich information that can be derived from a single CCTA study. Beyond merely identifying the presence of CAD and assessing stenosis severity, CCTA now allows for the identification and characterization of plaques, lesion length, and fluoroscopic angle optimization, as well as enables the assessment of the physiologic extent of stenosis through CT-derived fractional flow reserve, and may even allow for the prediction of the response to revascularization. These and other features make CCTA capable of not only guiding invasive coronary angiography referral, but also give it the unique ability to help plan coronary intervention. This review summarizes current and future applications of CCTA in procedural planning for percutaneous coronary intervention, provides rationale for wider integration of CCTA in the workflow of the interventional cardiologist, and details how CCTA may help improve patient care and clinical outcomes. Keywords: CT Angiography © RSNA, 2022
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.012 |
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".