Is there a role for fractional flow reserve in coronary artery bypass graft (CABG) planning?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The concept of significant lesions has substantially evolved over the last decade. With growing evidence for use of fractional flow reserve (FFR) as a determinant of lesion-specific ischemia and its superiority to angiography-guided revascularization and medical therapy, the field of percutaneous revascularization has shifted to rely exclusively on FFR instead of luminal stenosis alone in guiding revascularization. This transition to physiological assessment has not yet made it to the realm of surgical revascularization. FFR-guided therapy has been shown to be superior to angiography-guided therapy mainly by safe deferral of about 1/3rd of lesions, leading to less periprocedural events and better outcomes. Is it possible that utilization of FFR-guided CABG would lead to less complicated procedures, shorter operating times, more frequent off pump CABG procedures and more hybrid procedures? Can FFR-guided CABG improve the cardiovascular outcomes as compared to current standard of practice? In the following paragraphs we review the concept of FFR, the evidence behind FFR-guided therapy, the emerging data regarding use FFR-guided CABG and discuss where the revascularization field is headed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 it