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Record W1516240356 · doi:10.1111/ajt.12667

The Role of Donor-Specific HLA Alloantibodies in Liver Transplantation

2014· article· en· W1516240356 on OpenAlexaff
Jacqueline G. O’Leary, Anthony J. Demetris, L S Friedman, Howard M. Gebel, Philip F. Halloran, Allan D. Kirk, Stuart J. Knechtle, S V McDiarmid, Abraham Shaked, Paul I. Terasaki, Kathryn Tinckam, Stephen J. Tomlanovich, Kathryn J. Wood, E. Steve Woodle, Andrea A. Zachary, Göran B. Klintmalm

Bibliographic record

VenueAmerican Journal of Transplantation · 2014
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity Health NetworkUniversity of AlbertaThe Metabolomics Innovation Centre
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesGenentechAstellas Pharma
KeywordsMedicineLiver transplantationHuman leukocyte antigenTransplantationImmunologyHepatitis BLiver diseaseInternal medicineAntigen

Abstract

fetched live from OpenAlex

The impact of donor-specific HLA alloantibodies (DSA) on short- and long-term liver transplant outcome is not clearly defined. While it is clear that not all levels of allosensitization produce overt clinical injury, and that liver allografts possess some degree of alloantibody resistance, alloantibody-mediated adverse consequences are increasingly being recognized. To better define the current state of this topic, we assembled experts to provide insights, explore controversies and develop recommendations for future research on the consequences of DSA in liver transplantation. This article summarizes the proceedings of this inaugural meeting. Several insights emerged. Acute antibody-mediated rejection (AMR), although rarely diagnosed, is increasingly understood to overlap with T cell–mediated rejection. Isolated liver allograft recipients are at increased risk of early allograft immunologic injury when preformed DSA are high titer and persist posttransplantation. Persons who undergo simultaneous liver–kidney transplantation are at risk of renal AMR when Class II DSA persist posttransplantation. Other under-appreciated DSA associations include ductopenia and fibrosis, plasma cell hepatitis, biliary strictures and accelerated fibrosis associated with recurrent liver disease. Standardized DSA testing and diagnostic criteria for both acute and chronic AMR are needed to distil existing associations into etiological processes in order to develop responsive therapeutic strategies. The impact of donor-specific HLA alloantibodies (DSA) on short- and long-term liver transplant outcome is not clearly defined. While it is clear that not all levels of allosensitization produce overt clinical injury, and that liver allografts possess some degree of alloantibody resistance, alloantibody-mediated adverse consequences are increasingly being recognized. To better define the current state of this topic, we assembled experts to provide insights, explore controversies and develop recommendations for future research on the consequences of DSA in liver transplantation. This article summarizes the proceedings of this inaugural meeting. Several insights emerged. Acute antibody-mediated rejection (AMR), although rarely diagnosed, is increasingly understood to overlap with T cell–mediated rejection. Isolated liver allograft recipients are at increased risk of early allograft immunologic injury when preformed DSA are high titer and persist posttransplantation. Persons who undergo simultaneous liver–kidney transplantation are at risk of renal AMR when Class II DSA persist posttransplantation. Other under-appreciated DSA associations include ductopenia and fibrosis, plasma cell hepatitis, biliary strictures and accelerated fibrosis associated with recurrent liver disease. Standardized DSA testing and diagnostic criteria for both acute and chronic AMR are needed to distil existing associations into etiological processes in order to develop responsive therapeutic strategies.

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 score0.495
Threshold uncertainty score0.354

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 head0.008
GPT teacher head0.252
Teacher spread0.244 · 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

Citations210
Published2014
Admission routes1
Has abstractyes

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