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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.003
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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