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Record W2097115382 · doi:10.1681/asn.2014080837

Utility of HLA Antibody Testing in Kidney Transplantation

2015· review· en· W2097115382 on OpenAlexaff
Ana Konvalinka, Kathryn Tinckam

Bibliographic record

VenueJournal of the American Society of Nephrology · 2015
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsHuman leukocyte antigenImmunologyAntibodyKidney transplantationMedicineTransplantationAntigenAlloimmunityHistocompatibility TestingPanel reactive antibodyImmune systemInternal medicine

Abstract

fetched live from OpenAlex

HLA antigens are polymorphic proteins expressed on donor kidney allograft endothelium and are critical targets for recipient immune recognition. HLA antibodies are risk factors for acute and chronic rejection and allograft loss. Solid-phase immunoassays for HLA antibody detection represent a major advance in sensitivity and specificity over cell-based methods and are widely used in organ allocation and pretransplant risk assessment. Post-transplant, development of de novo donor-specific HLA antibodies and/or increase in donor-specific antibodies from pretransplant levels are associated with adverse outcomes. Although single antigen bead assays have allowed sensitive detection of recipient HLA antibodies and their specificities, a number of interpretive considerations must be appreciated to understand test results in clinical and research contexts. This review, which is especially relevant for clinicians caring for transplant patients, discusses the technical aspects of single antigen bead assays, emphasizes their quantitative limitations, and explores the utility of HLA antibody testing in identifying and managing important pre- and post-transplant clinical outcomes.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.830
Threshold uncertainty score0.482

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
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 head0.084
GPT teacher head0.400
Teacher spread0.316 · 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 designOther design
Domainnot available
GenreReview

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

Citations183
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueJournal of the American Society of NephrologySame topicRenal Transplantation Outcomes and TreatmentsFrench-language works237,207