Cadaveric training model for the endovascular management of type B aortic dissection
Bibliographic record
Abstract
Background: The surgical management of type-B aortic dissection (TBAD) poses considerable technical challenges, necessitating meticulous planning and precise execution. In an effort to enhance the proficiency of trainees in the management of TBAD through thoracic aortic endovascular repair, we have developed a cadaveric TBAD training model. Methods: We conducted a feasibility test using a plastic tube designed to simulate the basic anatomical characteristics of the aorta. To access the interior of the tube, we introduced a 26 Fr and a 5 Fr sheath at each end. Employing a soft glidewire, we fashioned a proximal loop around the proximal segment of a Dacron graft (DG). Subsequently, a distal loop was created at the distal end of the DG using a glidewire. The DG was then carefully maneuvered through the 26 Fr sheath within the simulated "aorta" by traction on the distal end of the proximal loop, which extended outward from the 5 Fr sheath. Finally, visualization of the DG within the "aorta" was achieved using an intravascular ultrasound catheter. This methodology was subsequently replicated in a cadaveric model, as detailed in the following section. Results: The in vitro feasibility test substantiated the viability of the devised concept for TBAD model creation. Encouraged by these findings, we proceeded to establish a cadaveric TBAD model. Access was gained to the left common carotid and right common femoral arteries, facilitating the placement of an undersized DG distal to the left subclavian artery, employing the previously described methodology. Completion angiography verified the successful creation of the TBAD model. In the conclusive phase, a Gore cTAG endograft was deployed distal to the left subclavian artery. Conclusions: The presented model not only demonstrated the feasibility of our conceptual approach for TBAD training model creation but also underscored the potential reproducibility of a cadaveric TBAD model. This innovative educational tool holds promise for effectively instructing vascular trainees in the intricate nuances of surgical management for TBAD.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".