Graft flow evaluation with intraoperative transit-time flow measurement in off-pump versus on-pump coronary artery bypass grafting
Bibliographic record
Abstract
Objective We aimed to compare transit-time flow measurement (TTFM) parameters for on-pump (ONCAB) and off-pump (OPCAB) coronary artery bypass procedures. Methods The database of the Registry for Quality AssESsmenT with Ultrasound Imaging and TTFM in Cardiac Bypass Surgery (REQUEST) study was retrospectively reviewed. Only single grafts were included (ie, no sequential or Y/T grafts). Primary end points were mean graft flow (MGF), pulsatility index (PI), diastolic fraction (DF), and backflow (BF). Unadjusted and propensity score-matching comparisons were performed. Results Of 1016 patients in the REQUEST registry, 846 had at least 1 graft for which TTFM was performed. Of these, 512 patients (60.6%) underwent ONCAB and 334 (39.4%) OPCAB procedures. Mean arterial pressure (MAP) during measurements was higher in the OPCAB group. After propensity score-matching, 312 well balanced pairs were left. In these matched patients, MGF was higher for the ONCAB versus the OPCAB group (32 vs 28 mL/min, respectively, for all grafts [ P < .001]; 30 vs 27 mL/min for arterial grafts [ P = .002]; and 35 vs 31 mL/min for venous grafts [ P = .006], respectively). PI was lower in the ONCAB group (2.1 vs 2.3, for all grafts; P < .001). Diastolic fraction was slightly lower in the ONCAB group (65% vs 67.5%; P < .001). The backflow was also lower in the ONCAB group (0.6 vs 1.3; P < .001) with trends similar to MGF and PI for venous and arterial grafts. There were 21 (3.3%) revisions in the OPCAB group and 14 (2.1%) in the ONCAB group ( P = .198). Conclusions ONCAB surgery was associated with higher MGF and lower PI values, especially in venous grafts. Different TTFM cutoff values for ONCAB versus OPCAB surgery might be considered.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".