Shear wave elasticity imaging for residual endoleak and thrombus characterisation after endoleak embolisation following endovascular aneurysm repair: a canine animal study
Bibliographic record
Abstract
BACKGROUND: To evaluate residual endoleak and thrombus organisation with shear wave imaging (SWI) after endoleak embolisation through an animal study. METHODS: This prospective experimental study involved eight dogs with creation of 16 iliac aneurysms and type I endoleak after endovascular aneurysm repair (EVAR). Embolisation agents were injected into the sac to seal endoleak. SWI and colour flow Doppler ultrasound (DUS) were performed at implantation, one week, and one and three months after implantation; for three dogs, SWI and DUS were also performed six months after implantation. Digital subtraction angiography and contrast-enhanced computed tomography were performed at sacrifice. Macroscopic and histopathological analyses were processed to identify regions of interest (ROIs) for endoleak, fresh thrombus, organised thrombus and embolisation agent, where SWI elasticity moduli were compared. RESULTS: At sacrifice, nine aneurysms had residual endoleak, while seven were sealed. Ten had a fresh and 15 had an organised thrombus. SWI was able to detect all endoleaks, including two cases undetected with DUS. Elasticity moduli of 0.2 kPa ± 0.1 kPa (mean ± SD), 9.5 kPa ± 3.3 kPa, 48.1 kPa ± 21.3 kPa and 44.9 kPa ± 23.7 kPa were found in the ROIs positioned in endoleaks, fresh thrombi, organised thrombi and embolisation agent, respectively. Elasticity values of endoleak and fresh thrombus were lower than those of organised thrombi and embolisation agent (p < 0.001). Stiffness of fresh thrombus at one week (8.7 kPa ± 3.6 kPa) increased at three months (30.2 kPa ± 13.8 kPa), indicating thrombus maturation (p < 0.001). CONCLUSIONS: In a dog model of iliac EVAR, SWI was able to identify endoleak, thrombus maturation and embolising agents after endoleak embolisation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".