Endovascular treatment of arteriovenous graft pseudoaneurysms, indications, complications, and outcomes: A systematic review
Bibliographic record
Abstract
There are limited data regarding endovascular treatment of arteriovenous graft (AVG) pseudoaneurysms using stent grafts. We performed a comprehensive literature review on the use of stent grafts in the treatment of AVG pseudoaneurysms. We included 10 studies (121 patients). The mean AVG age was 3.1 years (95% confidence interval [CI]: 2.2-4) and pseudoaneurysm mean diameter was 34 mm (95% CI: 23-46). The majority (71%) of the pseudoaneurysms were located on the arterial limb of the AVG and 77% presented with venous anastomosis stenosis requiring angioplasty. The mean number of stents used to treat one lesion was 1.4 (95% CI: 1.3-1.5). The technical success rate of pseudoaneurysm isolation was 100% in all studies and 100% of patients received hemodialysis using the AVG after pseudoaneurysm treatment without the need for catheter placement. The primary patency rates for 1, 3, and 6 months were 81%, 73%, and 24%. Secondary patency was 80%, 77%, and 74% at 1, 3, and 6 months. Arteriovenous graft thrombosis occurred in 12% of patients. Arteriovenous graft infection developed in 35% of cases. Arteriovenous graft pseudoaneurysm treatment using stent grafts is effective in managing even large pseudoaneurysms and has acceptable primary and secondary patency rates. Graft infection was a relatively frequent complication.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".