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Ethnicity, sex, and age are determinants of red blood cell storage and stress hemolysis: results of the REDS-III RBC-Omics study

2017· article· en· W2668200717 on OpenAlexaff
Tamir Kanias, Marion C. Lanteri, Grier P. Page, Yuelong Guo, Stacy Endres‐Dighe, Mars Stone, Sheila M. Keating, Alan E. Mast, Ritchard G. Cable, Darrell J. Triulzi, Joseph E. Kiss, Edward L. Murphy, Steve Kleinman, Michael P. Busch, Mark T. Gladwin

Bibliographic record

VenueBlood Advances · 2017
Typearticle
Languageen
FieldMedicine
TopicBlood groups and transfusion
Canadian institutionsUniversity of VictoriaUniversity of British Columbia
FundersNational Heart, Lung, and Blood InstituteRTI InternationalNational Institutes of HealthBlood Systems Research InstituteUniversity of California, San FranciscoInstitute For Transfusion MedicineUniversity of Pittsburgh
KeywordsHemolysisEthnic groupRed blood cellOmicsMedicineGenetic predispositionGerontologyBiologyImmunologyDemographyInternal medicineBioinformaticsPolitical science

Abstract

fetched live from OpenAlex

< .0001). Donor race/ethnicity was also associated with extreme (>1%) levels of storage hemolysis exceeding US Food and Drug Administration regulations for transfusion (hemolysis >1% was observed in 3.51% of Asian and 2.47% of African American donors vs 1.67% of white donors). These findings highlight the impact of donor genetic traits on measures of RBC hemolysis during routine cold storage, and they support current plans for genome-wide association studies, which may help identify hereditable variants with substantive effects on RBC storage stability and possibly posttransfusion 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)0.001
metaresearch head score (Gemma)0.002
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 score0.004
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.016
GPT teacher head0.276
Teacher spread0.259 · 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

Citations223
Published2017
Admission routes1
Has abstractyes

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