Radiological Evaluation of Abdominal Endovascular Aortic Aneurysm Repair
Bibliographic record
Abstract
Endovascular aortic aneurysm repair (EVAR) is an alternative to open surgical repair of aortic aneurysms offering lower perioperative mortality and morbidity. As experience increases, clinicians are undertaking complex repairs with hostile aortic anatomy using branched or fenestrated devices or extra components such as chimneys to ensure perfusion to visceral branch vessels whilst excluding the aneurysm. Defining the success of EVAR depends on both clinical and radiographic criteria, but ultimately depends on complete exclusion of the aneurysm from the circulation. Aortic stent grafts are monitored using a combination of imaging modalities including computed tomography angiography (CTA), ultrasonography, magnetic resonance imaging, plain films, and nuclear medicine studies. This article describes when and how to evaluate aortic stent grafts using each of these modalities along with the characteristic features of several of the main stent grafts currently used in clinical practice. The commonly encountered complications from EVAR are also discussed and how they can be detected using each imaging modality. As the radiation burden from serial follow up CTA imaging is now becoming a concern, different follow-up imaging strategies are proposed depending on the complexity of the repair and based on the relative merits and disadvantages of each imaging modality.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".