Re-vascularization may not increase graft survival after hepatic artery thrombosis in liver transplant recipients.
Bibliographic record
Abstract
BACKGROUND AND AIM: Hepatic artery thrombosis (HAT) occurs in 3% to 11% of all liver transplantations. Some authors have reported good outcomes with early thrombectomy. To investgate the impact of re-vascularization on graft survival. METHODS: A total of 566 primary, cadaveric, single organ, adult liver transplants were performed. Hepatic arterial Doppler was performed routinely and patients with abnormal findings during the first two post-operative weeks were reexplored. Abnormal findings after this time-point were verified by non-invasive angiogram. The 47 patients that were diagnosed with arterial thrombosis, either intra-operatively or by angiogram, were divided into three groups. No further action was taken for group A, thrombectomy alone was performed for group B1, thrombectomy and anastomotic revision was employed for group B2. RESULTS: Arterial thrombosis was diagnosed in 47 (8.3%) patients. Mean patient survival was 42, 62 and 98 months for groups A, B1 and B2 respectively (p: 0.0629). Mean graft survival was 24, 29 and 60 months for groups A, B1 and B2 respectively (p: 0.3386). Re-transplant incidence was 8.7%, 40% and 28.6% for groups A, B1, and B2 respectively (p: 0.035). CONCLUSIONS: Early diagnosis of HAT by surveillance Doppler may lead to improved recipient survival secondary to earlier re-transplantation and not because of successful graft re-vascularization.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".