Successful Coronary Artery Bypass Grafting Based Solely on Non-Invasive Coronary Computed Tomography Angiography
Bibliographic record
Abstract
An 81-year-old female presented with chronic coronary disease (Canadian Cardiovascular Society angina severity grading III). The patient underwent coronary computed tomography angiography (CCTA) that revealed three-vessel coronary artery disease (3VD). This case illustrates that in a patient with 3VD, planning and execution of coronary artery bypass grafting (CABG) were successfully performed based solely on CCTA combined with fractional flow reserve derived from computed tomography angiography (FFRCT). Coronary artery bypass grafting (CABG) was planned and executed as follows: left internal mammary artery grafted to the left anterior descending artery (LAD), saphenous vein graft (SVG) to the right coronary artery (RCA), and SVG to the obtuse marginal artery (OM). Repeat imaging assessment with non-invasive CCTA and FFRCT at 30-day follow-up confirmed the safety of this approach. The FFRCT values of the RCA and LAD were normalized, whereas a borderline pressure drop was observed in the distal run-off of the OM (FFRCT=0.79). Notably, this is the first case in which post-CABG FFRCT assessment was performed. Post-CABG FFRCT is an investigational novel non-invasive tool for assessing the functional improvement of the epicardial conductance vessels following surgical revascularization.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".