Management of hepatic artery thrombosis and stenosis after pediatric liver transplantation: Variability and agreement in management practices
Bibliographic record
Abstract
Guidelines for managing hepatic artery thrombosis (HAT) and stenosis (HAS) after pediatric liver transplantation (pLT) are lacking, with heterogeneous local practices. This study aims to evaluate management practices for HAT and HAS after pLT. An online and paper-based survey was sent to 36 international pLT centers. The survey included 36 questions covering center experience, screening protocols, diagnostic criteria, preventive management, post-procedural care, and follow-up. Treatment strategies were explored through hypothetical case scenarios categorized by early (≤14 d after pLT) and late onset complications (>14 d after pLT). Responses from 36 centers showed that 60% applied interrupted sutures and 76% used a surgical loupe during transplantation. In addition, 89% followed a specific anticoagulation protocol after uncomplicated pLT. All centers initiated Doppler ultrasound (DUS) within 24 hours after pLT, with 60% conducting it daily during the first week. Immediate re-transplantation was preferred for early HAT with pediatric acute liver failure (PALF) (61% vs. 11% for non-PALF, p <0.001), and surgical revascularization was more frequently chosen for non-PALF cases (51% vs. 24% for PALF, p <0.001). Endovascular therapy was selected in 35% of cases for both late HAT and HAS, with conservative management chosen in 51% for late HAT and 61% for late HAS (all p <0.001, compared to early cases). Internationally, there is agreement on the importance of early DUS screening in current management practices. Immediate re-transplantation was preferred for early HAT with PALF, while surgical revascularization was favored for non-PALF cases. Conservative management and endovascular therapy emerged as potential strategies for late-onset cases. This worldwide survey on real-world practice provides a basis for developing and implementing guidelines.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".