Retroperitoneal Abdominal Aortic Repair With the da Vinci Single Port System: A Porcine Model Feasibility Study
Bibliographic record
Abstract
Objective: Robotic surgery is rarely used in vascular surgery but has demonstrated significant potential compared with open or laparoscopic techniques in reports of abdominal aortic surgery. Enabling a standardised retroperitoneal approach (RPA) to abdominal aortic repair might reduce the peri-operative mortality and morbidity rates associated with a transabdominal approach. The aim of this experimental study was to establish surgical techniques for robotically assisted infrarenal abdominal aortic repair using a single port RPA. Methods: Retroperitoneal abdominal aortic repair was performed in four live porcine models by a group of experienced and novice robotic surgeons. The procedures were performed using the da Vinci SP (single port) surgical system (Intuitive Surgical Inc., Sunnyvale, CA, USA). Polytetrafluoroethylene grafts (W.L. Gore & Associates, Inc., Newark, DE, USA) were implanted after aortic resection. Operating times for the main procedure steps were recorded. Results: Abdominal aortic repair was feasible in all porcine models applying an RPA through a single incision with the da Vinci SP system. Creating the retroperitoneal access and docking the da Vinci SP was quick, with durations ranging from eight to 20 minutes. Proximal and distal anastomoses were completed in 26 minutes (range 19-33 minutes) and 31.3 minutes (range 23-42 minutes), respectively. In porcine models 2-4, the anastomoses were patent without haemorrhage, with aortic pulsations confirmed on release of the cross clamps. Porcine model 1 was pulseless owing to uncontrolled haemorrhage caused by left external iliac artery penetration at the end of the procedure, and patency could not be tested. Conclusion: Retroperitoneal single port robotic aortic repair was technically feasible with high accuracy in live porcine models. Anastomotic times were reasonable and surgeon comfort level was adequate, which may enable transfer into clinical routine in the future when true clinical benefits will need to be proven in additional studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".