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Record W2079178739 · doi:10.1016/j.bbmt.2009.04.015

HLA-A Disparities Illustrate Challenges for Ranking the Impact of HLA Mismatches on Bone Marrow Transplant Outcomes in the United States

2009· article· en· W2079178739 on OpenAlexfundno aff
Lee Ann Baxter‐Lowe, Martin Maiers, Stephen R. Spellman, Michael Haagenson, Tao Wang, Marcelo Fernández-Viña, Steven G. E. Marsh, Mary M. Horowitz, Carolyn Katovich Hurley

Bibliographic record

VenueBiology of Blood and Marrow Transplantation · 2009
Typearticle
Languageen
FieldMedicine
TopicHematopoietic Stem Cell Transplantation
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institute of Allergy and Infectious DiseasesNational Cancer InstituteOffice of Naval ResearchTakeda OncologyUniversity of California, San FranciscoHealth Resources and Services AdministrationNational Institutes of HealthOtsuka PharmaceuticalAstellas PharmaTherakosKiadis PharmaAssociation of Medical Microbiology and Infectious Disease CanadaAmerican Society for Blood and Marrow TransplantationTeva Pharmaceutical IndustriesWellPointHistoGeneticsCellGenixMedical College of WisconsinStemCyteEnzon PharmaceuticalsU.S. NavyCenters for Disease Control and PreventionCelgeneOsiris TherapeuticsPfizerBaxter InternationalU.S. Department of Health and Human ServicesAstellas Pharma USAmgenBlue Cross and Blue Shield AssociationBe The Match FoundationBayer HealthCareU.S. Department of Defense
KeywordsHuman leukocyte antigenMedicineOdds ratioImmunologyConfidence intervalHistocompatibilityHistocompatibility TestingBone marrowHematopoietic stem cell transplantationBone marrow transplantationAlleleTransplantationInternal medicineGeneticsBiologyAntigen

Abstract

fetched live from OpenAlex

HLA disparity between hematopoietic stem cell donors and recipients is one of the most important factors influencing transplant outcomes, but there are no well-accepted guidelines to aid in selecting the optimal donor among several HLA mismatched donors. In this report, HLA-A is used as a model to illustrate factors that are barriers to delineating the relationship between specific HLA mismatches and transplant outcomes in the United States. Patients in this investigation received transplants for hematologic malignancies that were facilitated by the National Marrow Donor Program (NMDP) between 1990 and 2002 (n = 4226). High-resolution HLA typing was performed for HLA-A, -B, -C, -DRB1, -DQA1, -DQB1, -DPA1, and -DPB1. HLA-A mismatches were observed in 745 donor-recipient pairs and 62% of these pairs also had disparities at HLA-B, -C, and/or -DRB1. The HLA-A mismatches involved 190 different combinations of HLA-A alleles and 51% of these were observed in only 1 pair. Addition of a single HLA-A disparity when HLA-B, -C, and -DRB1 were matched (n = 282) was associated with increased mortality (odds ratio [OR] = 1.32, confidence interval [CI] 1.07-1.63). When HLA-B, -C, and -DRB1 were matched, the most frequent HLA-A mismatches were HLA-A*0201:0205 (n = 28), HLA-A *0301:0302 (n = 15), HLA-A *0201:0206 (n = 15), HLA-A *0201:6801 (n = 12), HLA-A*0101:1101 (n = 11), and HLA-A*0101:0201 (n = 10). There were no statistically significant relationships between any of these disparities and transplant outcomes (engraftment, acute and chronic graft-versus-host disease [aGVHD, cGVHD] relapse, treatment-related mortality [TRM], or overall survival [OS]) when adjustments for multiple comparisons were considered. Achieving 80% power to detect an effect of any 1 of these 6 HLA-A disparities on survival is estimated to require a total transplant population of 11,000 to more than 1 million U.S. donor-recipient pairs depending upon the HLA disparity. Thus, alternative approaches are required to develop a clinically relevant ranking system for specific HLA disparities in the United States.

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)0.007
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.039

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.015
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.033
GPT teacher head0.297
Teacher spread0.263 · 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 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

Citations26
Published2009
Admission routes1
Has abstractyes

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