Fractional flow reserve<i>to guide and to assess</i>coronary artery bypass grafting
Bibliographic record
Abstract
The aim of this review is to highlight the role of invasive functional evaluation in patients in whom coronary artery bypass graft (CABG) is indicated, and to examine the clinical evidence available in favour of fractional flow reserve (FFR) adoption in these patients, outline appropriate use, as well as point out potential pitfalls. FFR after CABG will also be reviewed, highlighting its correct interpretation and adoption when applied to both native coronary arteries and bypass grafts. Practice European guidelines support the use of FFR to complement coronary angiography with the highest degree of recommendation (Class IA) for the assessment of coronary stenosis before undertaking myocardial revascularization when previous non-invasive functional evaluation is unavailable or not conclusive. As a result, FFR has been adopted in routine clinical practice to guide clinicians decision as to whether or not perform a revascularization. Of note, due to the increasing confidence of the interventional cardiologists, FFR guidance is also being implemented to indicate or guide CABG. This is in anticipation of supportive clear-cut evidence, since recommendations for FFR adoption were based on randomized clinical trials investigating percutaneous coronary intervention (PCI) strategies in which patients with typical indications for CABG were excluded (e.g. left main disease, valvular disease, and coronary anatomy unsuitable for PCI). Based on the critical appraisal of the literature, FFR can play an important role in risk stratification and determining management strategy of patients either before or after 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 | 0.002 | 0.005 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".