A cadaveric perfused model with antegrade arteriovenous pulsatile circulation: a new tool for teaching endovascular skills
Bibliographic record
Abstract
BACKGROUND: The benefits of using cadaveric humans in surgical training are well documented, and knowledge of the latest endovascular techniques is essential in the daily practice of vascular surgeons. Our study explores the feasibility of an affordable human cadaveric model with pulsatile and heated antegrade perfusion for reliable and reproducible endovascular or surgical simulation. METHODS: We undertook cannulation of 7 human cadavers embalmed in a saturated salt solution to create a left-to-right central perfusion with a heated solution, from the ascending thoracic aorta to the right atrium. To that end, we used surgically created carotidojugular and femorofemoral arteriovenous fistulas. Biomedical engineers designed a prototype pump for pulsatile circulation. We monitored invasive blood pressure and temperature. We used this model for training for endovascular thoracic aortic procedures and open vascular surgeries. RESULTS: The prototype pump achieved a pulsatile flow rate of 4.7 L/min. Effective cadaveric perfusion was achieved for several hours, not only with an arterioarterial pathway but also with arteriovenous circulation. The arterial pressures and in situ temperatures accurately restored vascular functions for life-like conditions. This new model made it possible to successfully perform thoracic endovascular aortic repair, subclavian artery stenting and simulation of abdominal open vascular trauma management. The saturated salt solution method and a specifically designed pump improved cost competitiveness. CONCLUSION: Endovascular simulation on human cadavers, optimized with the pulsatile and heated perfusion system, can be a dynamic adjunct for surgical training and familiarization with new devices. This reproducible teaching tool could be relevant in all surgery programs.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".