Risk factors for and management of ischemic-type biliary lesions following orthotopic liver transplantation: A single center experience
Bibliographic record
Abstract
INTRODUCTION: Biliary complications can cause morbidity, graft loss, and mortality after liver transplantation. The most troublesome biliary complications are ischemic-type biliary lesions (ITBL), which occur since transplants can now be performed after the donor has undergone circulatory death. The exact origin of this type of biliary complication remains unknown. MATERIAL AND METHODS: A total of 528 patients were retrospectively analyzed following liver transplantation after excluding 30 patients with primary sclerosing cholangitis and those lost to follow-up from January 2007 to January 2014. The incidence of and risk factors for ITBL were evaluated. RESULTS: Cold ischemia time (CIT) (P = 0.042) and warm ischemia time (WIT) (P = 0.006) were found to be independent risk factors for the development of ITBL. Use of the cytochrome P450 (CYP) 3A5 genotype assay to guide individualization of immunosuppressive medications resulted in significantly fewer ITBL (P = 0.027. Autoimmune hepatitis might be a risk factor for ITBL, as determined using univariate analysis (P = 0.047). CONCLUSIONS: Efforts should be taken to minimize risk factors associated with ITBL, such as CIT and WIT. The CYP3A5 genotype assay should be used to guide selection of immunosuppressive therapy in an effort to reduce the occurrence of ITBL.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".