How to Handle Arterial Conduits in Liver Transplantation? Evidence From the First Multicenter Risk Analysis
Bibliographic record
Abstract
OBJECTIVE: The aims of the present study were to identify independent risk factors for conduit occlusion, compare outcomes of different AC placement sites, and investigate whether postoperative platelet antiaggregation is protective. BACKGROUND: Arterial conduits (AC) in liver transplantation (LT) offer an effective rescue option when regular arterial graft revascularization is not feasible. However, the role of the conduit placement site and postoperative antiaggregation is insufficiently answered in the literature. STUDY DESIGN: This is an international, multicenter cohort study of adult deceased donor LT requiring AC. The study included 14 LT centers and covered the period from January 2007 to December 2016. Primary endpoint was arterial occlusion/patency. Secondary endpoints included intra- and perioperative outcomes and graft and patient survival. RESULTS: The cohort was composed of 565 LT. Infrarenal aortic placement was performed in 77% of ACs whereas supraceliac placement in 20%. Early occlusion (≤30 days) occurred in 8% of cases. Primary patency was equivalent for supraceliac, infrarenal, and iliac conduits. Multivariate analysis identified donor age >40 years, coronary artery bypass, and no aspirin after LT as independent risk factors for early occlusion. Postoperative antiaggregation regimen differed among centers and was given in 49% of cases. Graft survival was significantly superior for patients receiving aggregation inhibitors after LT. CONCLUSION: When AC is required for rescue graft revascularization, the conduit placement site seems to be negligible and should follow the surgeon's preference. In this high-risk group, the study supports the concept of postoperative antiaggregation in LT requiring AC.
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 | 0.015 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".