Endovascular Repair of Abdominal Aortic Aneurysm: Follow-up with Noninvasive Vascular Elastography in a Canine Model
Bibliographic record
Abstract
PURPOSE: To assess the ability of noninvasive vascular elastography (NIVE) to help characterize endoleaks and thrombus organization in a canine model of abdominal aortic aneurysm after endovascular aneurysm repair with stent-grafts, in comparison with computed tomography (CT) and pathologic examination findings. MATERIALS AND METHODS: All protocols were approved by the Animal Care Committee in accordance with the guidelines of the Canadian Council of Animal Care. Stent-grafts were implanted in a group of 18 dogs with aneurysms created in the abdominal aorta. Type I endoleak was created in four aneurysms; type II endoleak, in 13 aneurysms; and no endoleak, in one aneurysm. Doppler ultrasonography and NIVE examinations were performed at baseline and at 1-week, 1-month, 3-month, and 6-month follow-up. Angiography, CT, and macroscopic tissue examination were performed at sacrifice. Strain values were computed by using the Lagrangian speckle model estimator. Areas of endoleak, solid organized thrombus, and fresh thrombus were identified and segmented by comparing the results of CT and macroscopic tissue examination. Strain values were compared by using the Wilcoxon rank-sum and Kruskal-Wallis tests. RESULTS: All stent-grafts were successfully deployed, and endoleaks were clearly depicted in the last follow-up elastography examinations. Maximal axial strains over consecutive heart cycles in endoleak, organized thrombus, and fresh thrombus areas were 0.78% ± 0.22, 0.23% ± 0.02, 0.10% ± 0.04, respectively. Strain values were significantly different between endoleak and organized or fresh thrombus areas (P < .000) and between organized and fresh thrombus areas (P < .0002). No correlation was found between strain values and type of endoleak, sac pressure, endoleak size, and aneurysm size. CONCLUSION: NIVE may be able to help characterize endoleak and thrombus organization, regardless of the size, pressure, and type of endoleak.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".