An alternative arterial conduit for totally endoscopic multivessel coronary artery bypass.
Bibliographic record
Abstract
BACKGROUND: The ultimate goal of coronary artery bypass grafting (CABG) is the performance of a totally endoscopic procedure using multiple arterial conduits. At our center we have been routinely performing endoscopic robotic harvesting of internal thoracic arteries (ITAs) for use in minimally invasive CABG. The right gastroepiploic artery (RGEA) has been shown to be a reliable and versatile arterial conduit for bypass to coronary vessels not easily accessible by an ITA. The RGEA has already been harvested less invasively through a small laparotomy. This procedure could be made even less invasive by harvesting the RGEA laparoscopically, but this procedure has not yet been reported. The purpose of this study was to develop an endoscopic technique for harvesting the RGEA and demonstrate the safety and efficacy of this less invasive approach. METHODS: Twenty Duroc X Hampshire swine were administered general anesthesia and intubated. Ten mm and 5 mm trocars were then inserted. A 10 mm, 30-degree endoscope was adapted to a voice-activated robotic arm (AESOP), and the RGEA was harvested totally endoscopically using 5 mm harmonic scalpel shears. Intraoperative events and RGEA harvest times were recorded, and RGEA flows were measured after harvest. RGEA was delivered into the pericardial sac endoscopically. RESULTS: All RGEAs were successfully harvested without injury. Harvest time averaged 29.9+/- 10.9 min. The harvested conduits averaged 24.7+/- 2.37 cm in length. Flows were excellent in all harvested conduits, averaging 81.1+/- 31.8 cc/min. The harmonic scalpel controlled all RGEA branches with excellent hemostasis. CONCLUSION: The RGEA can be harvested safely through port access with robotic assistance. This conduit is of sufficient length to be used as an alternative arterial conduit for totally endoscopic multivessel coronary artery bypass.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".