Optimal In Situ Fenestration Technique With Laser Perforation and Balloon Dilation for Aortic Stent-Grafts
Bibliographic record
Abstract
PURPOSE: To evaluate the response of various stent-grafts after laser fenestration and dilation with noncompliant balloons to determine the optimal therapeutic combination for this treatment technique. MATERIALS AND METHODS: Five aortic stent-grafts were evaluated ex vivo: the Bolton RelayPlus, Jotec E-vita Thoracic 3G, Medtronic Valiant, Cook Zenith Alpha, and Vascutek Anaconda. Small holes were created using an excimer laser with the grafts submerged in saline. Five rows of 5 fenestrations were created, 4 holes in each row were dilated once with a 6-, 8-, 10-, or 12-mm-diameter noncompliant balloon to the specified nominal pressure (one hole served as the control). The saline solution from each stent-graft was collected and qualitatively analyzed for debris. The fenestrations were evaluated under light and scanning electron microscopes. The maximum diameter and area for each fenestration were measured. The direction and length of tears were assessed. RESULTS: when using balloons of 6- and 8-mm diameter, respectively. The 10- and 12-mm-diameter balloons caused a significant increase in area, variability, and tearing. The Anaconda graft tended to tear in the weft direction, while the other devices tore in the warp direction when using the 10- and 12-mm-diameter balloons. Dilation of the RelayPlus and Anaconda grafts with 6- and 8-mm-diameter balloons provided minimal tearing and precise fenestrations. Melted fiber remnants were observed after filtration of the saline solution for all devices. CONCLUSION: Laser fenestration and dilation with noncompliant balloons is a relatively simple and reproducible option for revascularization in urgent, complex aortic endovascular repairs. In our model, large balloons (ie, >10 mm) increased the destruction and tearing of the fabric. The maximum dilation recommended is 6 to 8 mm to avoid significant tears. Development of stent-grafts or novel fabrics designed explicitly for fenestration is needed to reduce potential complications.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".