Intraoperative transit-time flow measurement and high-frequency ultrasound assessment in coronary artery bypass grafting
Bibliographic record
Abstract
OBJECTIVES: We evaluated the influence of transit-time flow measurement with epicardial and epiaortic high-frequency ultrasound in patients undergoing coronary artery bypass grafting procedure. METHODS: The Registry for Quality Assessment with Ultrasound Imaging and Transit-time Flow Measurement in Cardiac Bypass Surgery study is a multicenter, prospective study among 7 international centers performing coronary artery bypass grafting procedures. The primary end point was any change in the planned surgical procedure. Major secondary end points consisted of the rate and reason for surgical changes related to the aorta, in situ conduits, coronary targets, and completed grafts, and the rate of in-hospital mortality and major morbidity. RESULTS: Between April 2015 and December 2017, 1046 patients were enrolled. Of those, 1016 were included in the final analyses. Mean age was 65.9 years, 14.0% were women, and diabetes was present in 39.6%. Off-pump procedures were performed in 39.6% and bilateral internal thoracic arteries in 30.5%. The primary end point occurred in 25.2% of patients (n = 256) and in 77% (197 out of 256) this was based on transit-time flow measurement and/or high-frequency ultrasound. Surgical changes were related to the aorta in 9.9%, to in situ conduits in 2.7%, and the coronary targets in 22.6%. Graft revision occurred in 7.8%, including revisions of the proximal and/or distal anastomosis in 6.6%. In-hospital adverse event rates were 0.6% for mortality, 1.0% for cerebrovascular events, and 0.3% for myocardial infarction. CONCLUSIONS: Surgical changes related to the aorta, conduits, coronary targets, and anastomosis were made in 25% of patients. This was associated with low operative mortality and low major morbidity. Transit-time flow measurement and high-frequency ultrasound may improve the quality, safety, and efficacy of coronary artery bypass grafting procedures and should be considered as a routine procedural aspect.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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 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".