Analysis of a multicenter registry on evaluation of transit-time flow in coronary artery disease surgery
Bibliographic record
Abstract
Objective The Evaluation of Transit-Time Flow in Coronary Artery Disease Surgery (EFCAD) registry aims to assess the influence of transit-time flow measurement (TTFM) in daily practice. Methods EFCAD is a prospective, multicenter study involving 9 centers performing TTFM during isolated coronary artery bypass grafting. Primary end point was occurrence and risk factors of major adverse cardiac events, including perioperative myocardial infarction, urgent postoperative coronary angiogram and/or revascularization, and hospital mortality. Secondary end points were rate of graft revision during surgery and factors affecting graft flow. We respected the limit values set by the experts: mean graft flow >15 mL/minute and pulsatility index ≤5. Results Between May 2017 and March 2021, 1616 patients were registered in the EFCAD database. After review, 1414 were included for analyses. Of those, 1176 were eligible for primary end point analysis. Graft revision, mainly due to inadequate TTFM values, occurred in 2% (29 patients). The primary end point occurred in 46 (3.9%) patients, and it was related with left anterior descending artery graft flow ≤15 mL/minute (odds ratio, 3.64; P < .001). Graft flow was related with number of grafts (3 vs 1-2, β = −1.6; 4-6 vs 1-2, β = −4.1; P < .001; β > 0 indicates higher flow), and graft origin (aorta vs Y, β = 9.2; in situ left internal thoracic artery vs Y, β = 3.2; in situ right internal thoracic artery vs Y, β = 2.3; P < .001). Conclusions Data from EFCAD study suggest that TTFM is reliable to evaluate graft flow, and acceptance of inadequate flow on left anterior descending artery anastomosis influence postoperative outcomes. In our opinion, TTFM assessment should be routinely used in coronary artery bypass procedures, even if interpretation depends on learning curves.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".