Endovascular Management of Complex Splenic Aneurysm with the “Amplatzer” Embolic Platform: Application of Cone-beam Computed Tomography
Bibliographic record
Abstract
Splenic artery aneurysms have the highest prevalence of all visceral arterial aneurysms (0.04%e10%). Treatment indications comprise symptomatic aneurysms, aneurysms 2.5 cm, and the presence of portal hypertension [1]. Within the last few decades, different endovascular treatment options were established by using coils, vascular plugs, or stent grafts for aneurysms occlusion or exclusion from blood flow [2e5]. The success of any endovascular aneurysm treatment relies on the secure exclusion of the aneurysmal sac from arterial perfusion. Feeding afferent and draining efferent arteries (so termed front-door and back-door access) are mostly visualized through either pre-interventional computed tomography angiography (CTA) or single-plane digital subtraction angiography (DSA). Modern angiography suites offer cone-beam computed tomography (CT) functionality, which provides a 3-dimensional reconstruction of the relevant anatomy through a single injection rotational acquisition, essentially creating a volumetric data set that can be reviewed on the fly [6,7]. This technique has been shown to allow intraoperative assessment of, eg, stent-graft positioning or transcatheter arterial embolization [8,9]. We hereby demonstrated the usability of cone-beam CT for vascular assessment in splenic artery aneurysm embolization and demonstrated its potential benefits when compared with standard DSA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".