Should functional assessment of lesion severity be used to guide coronary bypass?
Bibliographic record
Abstract
PURPOSE OF REVIEW: The purpose of this article is to investigate the potential role of fractional flow reserve (FFR) to guide surgical revascularization. RECENT FINDINGS: Coronary artery bypass is planned and executed primarily based on angiographic coronary anatomy. FFR is the most well-established tool for functional assessment of coronary lesions. Randomized trials have demonstrated the benefit of FFR-guided percutaneous coronary intervention (PCI) to determine the ischemic burden of intermediate lesions. Surgically, FFR is predominantly used to determine the functional severity of intermediate lesions of the left anterior descending (LAD) coronary artery to establish candidacy for multivessel coronary bypass. The broader use of FFR will likely downgrade a proportion of coronary lesions, which may alter the overall management plan. Whether this will improve clinical outcomes remains to be seen. Importantly, bypass of functionally nonsignificant lesions predicts graft failure. However, graft failure in the context of sufficient native coronary flow may not impact negatively on clinical outcome. Thus, at this time, there are insufficient data to support the wider use of FFR to guide surgical grafting of non-LAD targets. It remains to be seen whether FFR can be used to optimize the use of arterial grafts or to guide complex revascularization strategies such as hybrid coronary revascularization. SUMMARY: FFR has become an invaluable tool for decision making for PCI in patients with stable ischemic heart disease. Beyond its use to assess an intermediate LAD lesion to establish candidacy for coronary bypass, at present there are insufficient data to support its wider use to guide 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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".