Cutting-Edge Coronary Imaging Guiding CABG
Bibliographic record
Abstract
Coronary artery disease (CAD) is one of the major causes of death in the worldwide population. 1 Revascularization via percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG) are current treatment modalities besides aggressive management of risk factors. Hence, accurate assessment and diagnosis of coronary artery disease is crucial in planning and implementing given treatment modality. Since the introduction of invasive coronary angiography (ICA) in 1958, it remains the most widely used modality to assess the anatomy and the extent of obstructive CAD. 2 In fact, indication for CABG or PCI and pre-procedural planning are commonly based on visualization of CAD via ICA. 3,4 While it allows assessment of coronary anatomy, degree of luminal obstruction, and blood flow, it is known to underestimate and/or overestimate lesion severity, especially for intermediate stenosis. 5 The major reason for this inaccurate eyeballing estimation is the transformation of a 3-dimensional (3D) lesion into a 2-dimensional image. Moreover, there is a significant interindividual examiner variation in the degree of lesion estimation. 6 Therefore, anatomical and morphological assessment of CAD is not only insufficient for understanding of the disease and coronary hemodynamics, but also for planning of complex interventions warranting additional functional and physiological assessment. Recent advances in invasive and noninvasive cardiac imaging such as fractional flow reserve (FFR), instantaneous wave-free ratio (iFR), intravascular ultrasound (IVUS), optical coherence tomography (OCT), near-infrared spectroscopy, coronary computed tomography angiogram (CCTA), positron emission tomography (PET), and single-photon emission computerized tomography myocardial perfusion imaging (MPI) allow more accurate assessment of a given lesion directing correct indication and planning of a given procedure. While their utility has been studied to variable extents in the context of PCI, there is a paucity, and in some of the modalities, there is a total absence of data in CABG.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".