Analysis of Iliac Artery Geometric Properties in Fenestrated Aortic Stent Graft Rotation
Bibliographic record
Abstract
INTRODUCTION: A complication of fenestrated endovascular aneurysm repair is the potential for stent graft rotation during deployment causing fenestration misalignment and branch artery occlusion. The objective of this study is to demonstrate that this rotation is caused by a buildup of rotational energy as the device is delivered through the iliac arteries and to quantify iliac artery geometric properties associated with device rotation. METHODS: A retrospective clinical study was undertaken in which iliac artery geometric properties were assessed from preoperative imaging for 42 cases divided into 2 groups: 27 in the nonrotation group and 15 in the rotation group. Preoperative computed tomography scans were segmented, and the iliac artery centerlines were determined. Iliac artery tortuosity, curvature, torsion, and diameter were calculated from the centerline and the segmented vessel geometry. RESULTS: ; P = .05). No statistically significant differences were found for the mean values of tortuosity, curvature, torsion, or diameter between the 2 groups. CONCLUSION: Stent graft rotation occurred in 36% of the cases considered in this study. Cases with high iliac artery total net torsion were found to be more likely to have stent graft rotation upon deployment. This retrospective study provides a framework for prospectively studying the influence of iliac artery geometric properties on fenestrated stent graft rotation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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".