Pre-procedural planning of coronary revascularization by cardiac computed tomography: An expert consensus document of the Society of Cardiovascular Computed Tomography
Bibliographic record
Abstract
Coronary CT angiography (CCTA) demonstrated high diagnostic accuracy for detecting coronary artery disease (CAD) and a key role in the management of patients with low-to-intermediate pretest likelihood of CAD. However, the clinical information provided by this noninvasive method is still regarded insufficient in patients with diffuse and complex CAD and for planning percutaneous coronary intervention (PCI) and surgical revascularization procedures. On the other hand, technology advancements have recently shown to improve CCTA diagnostic accuracy in patients with diffuse and calcific stenoses. Moreover, stress CT myocardial perfusion imaging (CT-MPI) and fractional flow reserve derived from CCTA (CT-FFR) have been introduced in clinical practice as new tools for evaluating the functional relevance of coronary stenoses, with the possibility to overcome the main CCTA drawback, i.e. anatomical assessment only. The potential value of CCTA to plan and guide interventional procedures lies in the wide range of information it can provide: a) detailed evaluation of plaque extension, volume and composition; b) prediction of procedural success of CTO PCI using scores derived from CCTA; c) identification of coronary lesions requiring additional techniques (e.g., atherectomy and lithotripsy) to improve stent implantation success by assessing calcium score and calcific plaque distribution; d) assessment of CCTA-derived Syntax Score and Syntax Score II, which allows to select the mode of revascularization (PCI or CABG) in patients with complex and multivessel CAD. The aim of this Consensus Document is to review and discuss the available data supporting the role of CCTA, CT-FFR and stress CT-MPI in the preprocedural and possibly intraprocedural planning and guidance of myocardial revascularization interventions.
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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 | 0.028 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".