Animal study on adjustable pre <b>-</b> fenestration aortic stent <b>-</b> graft.
Bibliographic record
Abstract
OBJECTIVES: To isolate aortic arch pathologies and keep the branches superior in arch patent, it always need physicians to do in-vivo fenestration or in-situ fenestration when performing the thoracic endovascular aortic repair (TEVAR). However, both of those fenestration techniques need structure modification of the stent-graft, which may affect their long-term stability. We designed an adjustable pre-fenestration aortic stent-graft to treat pathologies in this area and obtained a patent branch arteries. This study used this new designed stent-graft to perform fenestrated-TEVAR (f-TEVAR) in canine aorta. This study aims to identify its feasibility and safety through animal experiments, which might provide preliminary data for potential human implantation. METHODS: A total of 8 Labrador Retrievers were underwent f-TEVAR by using the new devices. Digital subtraction angiography was performed before and after f-TEVAR to evaluate the success of the procedures. All the canines were divided into a 4-week group (feeding for 4 weeks after operation) and a 12-week group (feeding for 12 weeks after operation). Computed tomography angiography (CTA) were performed before euthanasia. RESULTS: The success rate of operation was 100%. During the operation, there was no accident of major bleeding or failure to be released by fenestration. Adjustable fenestration worked well. No stent-graft migration was found in CTA at 4 weeks and 12 weeks after the operation. All branch arteries kept by the adjustable fenestration were patent. CONCLUSIONS: The result of feasibility and safety of the new designed adjustable pre-fenestration aortic stent-graft in f-TEVAR of canine is acceptable. This study provides a reference for further optimization of this stent and human f-TEVAR implantation involving aortic arch lesions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".