In Vitro Laser Fenestration of Aortic Stent‐Grafts: A Qualitative Analysis Under Scanning Electron Microscope
Bibliographic record
Abstract
In situ fenestration of stent-grafts allows patients with life threatening aortic pathologies to be amenable to emergent "off the shelf indications for use" percutaneous treatments as a bail out technique. Three types of aortic stent-grafts were subjected to laser fenestration in a physiological saline solution followed by balloon angioplasty using 8, 10 or 12 mm in diameter noncompliant balloons. The morphology and the size of fenestrations were observed under optical and scanning electron microscopy. The damage to the fabrics was analyzed and quantified. The creation of fenestrations was feasible in all devices, with varying degrees of fraying and/or tearing. The monofilament twill weave (Medtronic Valiant) tore in two directions (warp and weft) while the multifilament weave fenestrations showed more fraying (Anaconda Vascutek and Zenith TX2 Cook). The size and directions of tearing were more predictable with the 8 mm diameter balloon whereas the results obtained with the 10 and 12 mm diameter balloons were more unpredictable. The fenestrations were free of melting of the yarns and blackening of the filaments. The in situ fenestration is feasible but the observed damage to the fabric constructions must be carefully considered. This procedure must currently be limited to urgent and emergent life threatening cases because it is off indications for use for approved devices.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".