Percutaneous endoscopic transapical aortic valve implantation: three experimental transcatheter models
Bibliographic record
Abstract
We sought to demonstrate the feasibility of an endoscopic approach to transapical aortic valve implantation (AVI), avoiding the morbidity of a thoracotomy incision. Using an experimental pig model, we performed three different approaches to transapical AVI, using a standard minithoracotomy (n=4), a robotic approach using the da Vinci telemanipulator (n=4) and an endoscopic approach using a port and camera access (n=4). The feasibility of the different techniques, exposure of the left ventricular apex, postoperative blood loss and total operative time were evaluated. Left ventricular apical exposure, 'purse-string' suture control and 33-F introducer access were successfully performed and confirmed videoscopically, fluoroscopically and at a post mortem in all 12 animals. The haemodynamics were stable in all animals. Mean intraoperative and postoperative (two-hour) blood losses were 88 and 65 ml with minithoracotomy, and 228 and 138 ml with the robotic and 130 and 43 ml with the endoscopic technique (P=0.26, P=0.14, respectively). There was no significant change in perioperative haematocrit (P=0.53). The mean total operative times were 1.4, 3.9 and 1.1 h (P=0.06), respectively. Percutaneous endoscopic and robotic transapical AVI are both feasible and can be performed in a timely manner with reasonable perioperative blood loss. Future research will focus on identifying optimal candidates for surgery based upon preoperative thoracic imaging.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".