Stent-graft implantation for hepatic arterial bleeding: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Purpose The aim of this systematic review and meta-analysis was to identify the technical and clinical outcomes of stent-graft implantation in patients with hepatic artery bleeding. Materials and methods The PubMed database was searched for publications between 2000 and March 2025 evaluating patients treated with stent-graft implantation for hepatic arterial hemorrhage. The outcome measurements were technical (successful hemostasis with stent-grafts) and clinical success (no rebleeding from hepatic arteries), early mortality, and stent-graft patency during follow-up. A modified Newcastle–Ottawa Scale (NOS) was used to assess the quality of the included publications. An individual patient data meta-analysis was performed using the chi-square test or Fischer exact test to identify predictors of stent-graft patency. Results In total, 351 patients from 22 studies were included. The mean NOS score was 5.4 ± 0.95. Most patients (n = 323) had bleeding after surgery. The technical success rate of stent-graft placement was 94.3%. Patients with technical failure were managed either by surgery (n = 10) or coil embolization of the hepatic artery (n = 10). Rebleeding from hepatic arteries was seen in 24 patients with a clinical success rate of 92.7%. The early mortality rate was 15.6%. Follow-up showed stent-graft patency in 76.5% (202 of 264) of the cases. Most of the stent-graft occlusions (51/62, 82.2%) were asymptomatic. No significant difference was seen in the rate of stent-graft patency between patients receiving acetylsalicylic acid or not, or dual antiplatelet treatment or not. Overlapping stent-grafts (p = 0.009) were significant risk factors for stent-graft occlusion. Conclusion Stent-graft implantation in hepatic arterial bleeding is associated with high technical and clinical success rates. Despite the considerable rate of stent-graft occlusion during follow-up, occlusion is mostly asymptomatic, probably due to collateral development.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".