Factors Influencing Clinical Success Following Endovascular Treatment of Type II Endoleaks
Bibliographic record
Abstract
Purpose: To compare long-term outcomes of transarterial (TA) and translumbar (TL) embolization of type II endoleaks (T2E) following EVAR, as well as factors that predict clinical success. Methods: 129 (mean age, 71.4y; range, 53-95) with T2E referred for embolization from August-2003 to December-2017 were retrospectively reviewed. One-hundred-eighty procedures were performed via TA (n = 139) and TL (n = 41) approaches, with 37 patients undergoing 51 reinterventions. Clinical success was defined as absence of endoleak and/or absence of aneurysm sac enlargement on follow-up imaging. Medical comorbidities, procedural data, embolic agents used, presence of successful sac embolization, and 30-day morbidity and mortality data were collected. Results: TL approaches had higher technical success (41/41 vs.122/139, p = .014). Clinical success rates were 52% (N = 58/111) and 62% (N = 23/37) for TA and TL procedures respectively ( p = .34). Looking at all procedures, sac embolization using n-butyl cyanoacrylate glue had higher clinical success compared to other embolic agents ( p = .017-.037). Successful sac access was a strong predictor of success for TA procedures (46/78 vs.12/33, p = .0379). 30-day complication rates were similar between TA (5.8%) and TL (4.9%) approaches. There was 1 death secondary to graft infection following TA embolization. Conclusions: Overall clinical success of TA and TL embolization when considering re-interventions is high. n-butyl cyanoacrylate glue had significantly higher success than other embolic agents ( p = .017-.037). Successful sac access was associated with success for TA procedures.
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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 | 0.006 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".