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, b 1.6; 4-6 vs 1-2, b 4.1; P < .001;b > 0 indicates higher flow), and graft origin (aorta vs Y, b 9.2; in situ left internal thoracic artery vs Y, b 3.2; in situ right internal thoracic artery vs Y, b 2.3; P < .001). PERSPECTIVEData from EFCAD prospective multicenter registry suggest that TTFM is a reliable tool to evaluate graft flow and we found that postoperative adverse events are significantly higher in patients with inadequate ( 15 mL/minute) graft flow on LAD.Even if interpretation of TTFM assessment depends on learning curves and surgeon's commitment, it should be routinely adopted in CABG procedures.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".