The Retrograde-Access Gutter Snare (RAGS) Technique: A Bailout Maneuver to Maintain Patency of the Left Subclavian Artery in Fenestrated TEVAR
Bibliographic record
Abstract
PURPOSE: The purpose of this study is to describe a new bailout maneuver for use during fenestrated thoracic endovascular aneurysm repair (fTEVAR) in the event of wire wrap or wire entanglement with the proximal graft fabric. TECHNIQUE: A 68-year-old-man with hypertension and chronic atrial fibrillation underwent elective thoracic endovascular aneurysm repair (TEVAR) with a left subclavian fenestration to treat a residual arch and thoracic aortic aneurysm after previous type A dissection repair. The procedure was challenging due to malrotation of the main body graft, as well as wire entanglement of the precannulated through-and-through wire on the leading edge of the main body fabric. A novel bailout maneuver is described. Through-and-through access was maintained, and a long 8F sheath was delivered through the fenestration from femoral access, and an 8.5F steerable sheath was delivered through upper extremity access. This allowed coaxial snaring of a new through-and-through wire via the gutter between the stent-graft and native aorta in the seal zone, which both reoriented the fenestration, and permitted placement of a bridging stent to the left subclavian artery. CONCLUSION: The retrograde-access gutter snare (RAGS) technique described above provides a streamlined approach to achieve technical success in challenging fenestrated thoracic endovascular aneurysm repair (fTEVAR) cases.Clinical ImpactThis technical note describes the retrograde-access gutter snare (RAGS) technique that can provide a successful bailout maneuver for fenestrated TEVAR (fTEVAR) devices. The RAGS technique offers an approach to be used with precannulated custom-made or physician-modified fTEVAR devices in which wire wrap or wire entanglement precludes access through the fenestration after deployment of the main body device. Instead of traditional attempts at de novo cannulation of the fenestration from upper extremity or femoral access, the maneuver offers an approach to maintaining through-and-through access to facilitate snaring a new wire proximal to the leading edge of the main body fabric, which can then be used to deliver a covered stent into the fenestration from either the upper extremity or femoral access. This technique offers an added benefit of potentially realigning malrotated fenestrations via tensioning of the new through-and-through wire.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".