Utility of 4D CT in endoleak characterization after advanced endovascular aortic repair
Bibliographic record
Abstract
OBJECTIVES: To assess the performance of dynamic or 4D CT in characterizing endoleaks in advanced endovascular aortic repair (branched and fenestrated) when other modalities fail to fully characterize the leak, most often conventional CTA. METHODS: , with anywhere between 10 and 40 iterations performed every 2 s. These settings were adjusted depending on graft characteristics and type of endoleak suspected. The scans were assessed for their ability to detect the endoleak (sensitivity), and further to characterize the endoleak by type and subtype (specificity). RESULTS: Overall sensitivity in 16 scans for endoleak detection was 100%. There was a specificity of 87.5% for determining the type of endoleak (14/16). These results included two studies that were inconclusive and repeated due to technical difficulties. In patients where a specific subtype was not established, the leak was localized to the appropriate target vessel. Average dose for the 4D CT was 4724 mGy*cm (1108-11069), with the outlining higher dose scans secondary to higher iterations in those scans. CONCLUSIONS: 4D CT is a useful adjunctive tool in FB-EVAR surveillance with excellent sensitivity and specificity in characterizing endoleaks. This allows for accurate localization of leaks, which is critical for management planning.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".