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Record W2331990926 · doi:10.5604/16652681.1184204

Risk factors for and management of ischemic-type biliary lesions following orthotopic liver transplantation: A single center experience

2015· article· en· W2331990926 on OpenAlexfundno aff
Tao Jiang, Chuanyun Li, Binwei Duan, Yuan Liu, Lu Wang, Shichun Lu

Bibliographic record

VenueAnnals of Hepatology · 2015
Typearticle
Languageen
FieldMedicine
TopicOrgan Transplantation Techniques and Outcomes
Canadian institutionsnot available
FundersAlberta Innovates - Health Solutions
KeywordsMedicineOrthotopic liver transplantationSingle CenterLiver transplantationTransplantationCenter (category theory)SurgeryGastroenterologyInternal medicine

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.823
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.107
GPT teacher head0.358
Teacher spread0.251 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2015
Admission routes1
Has abstractyes

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