Intra-operative Graft Blood Flow Measurements for Composite and Sequential Coronary Artery Bypass Grafting
Bibliographic record
Abstract
OBJECTIVES: Intraoperative assessment of coronary artery bypass grafts (CABG) anastomotic quality can be performed using transit-time flowmetry (TTF). The aim of this study was to compare on- versus off-pump coronary graft TTF and early postoperative outcomes. MATERIALS AND METHODS: Between January 2009 and January 2010, 521 distal graft flows were assessed using TTF measurements in 253 consecutive patients undergoing primary isolated CABG surgery. Data were analyzed using multilevel models accounting for clustering among surgeons and grafts performed in the same patient. RESULTS: Mean age was 66 ± 10 years and 22% were female (n = 55) with 34% diabetics (n = 86) and 40% preoperative myocardial infarction (MI) (n = 101). The surgeries were performed off-pump in 67% (n = 170) with sequential vein grafts in 57% (n = 144) of patients. Off-pump patients had higher pre-operative left ventricular ejection fractions (LVEF), fewer urgent surgeries, fewer distal anastomoses, and fewer sequential vein grafts (all p<0.001). Intra-operative coronary graft TTF measurements were lower in sequential vein grafts performed off-pump versus on-pump. More patients in the on-pump group needed milrinone or dobutamine 24-48 h postoperatively (p = 0.005). Independent predictors of lower TTF included female gender and off-pump surgery, whereas predictors of better TTF were preoperative MI, larger coronary diameter at the site of the distal anastomosis, and sequential vein grafting. CONCLUSIONS: Lower intra-operative TTF measurements were found in sequential vein grafts in off-pump CABG. However, off-pump patients experienced similar short-term outcomes compared to on-pump patients.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".