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Record W3000683795 · doi:10.1111/trf.15666

The effect of serum pretreatment regimens for the detection of HLA class I antibodies in platelet‐refractory patients

2020· article· en· W3000683795 on OpenAlexaff
Gizem Tumer, Thomas J. Gniadek, Jennifer Baye, Ryan Pena, Paul Warner, Mark Fung, Lynette Beaudin, H. Dunckley, Manish J. Gandhi, Birgit Gathof, Susan H. Hsu, Ellen Klohe, Nalaja Marcus, Roberta Bamert, Shona Sims, Minoko Takanashi, Silvano Wendel, Claudia S. Cohn

Bibliographic record

VenueTransfusion · 2020
Typearticle
Languageen
FieldMedicine
TopicPlatelet Disorders and Treatments
Canadian institutionsCanadian Blood Services
Fundersnot available
KeywordsAntibodyCoefficient of variationHuman leukocyte antigenMedicinePlateletImmunologyAntigenRefractory (planetary science)Confidence intervalInternal medicineChemistryChromatographyBiology

Abstract

fetched live from OpenAlex

BACKGROUND: Single antigen bead (SAB) assays are used to identify human leukocyte antigen (HLA) antibodies in patients with platelet refractoriness due to HLA Class I alloimmunization. Some laboratories use serum pretreatment regimens to eliminate interference from immunoglobulin M antibodies and complement. These modifications may contribute to interlaboratory variability, which is a recognized problem with the SAB assay. STUDY DESIGN AND METHODS: Five patients' sera were overnight shipped to 12 laboratories in the United States and internationally. Recipients used their lab's SAB procedure to identify HLA Class I antibodies. The resultant mean fluorescence intensity (MFI) data were compared by instrumentation, bead lot, and pretreatment regimens. Laboratory-specific cutoffs for positive antibodies were applied to the results. RESULTS: Interlaboratory variability for MFI values appears to be associated with different pretreatment regimens. The coefficient of variation (CV) of MFI from samples pretreated with ethylenediaminetetraacetic acid, dithiothreitol, or heat inactivation (EDHI) were similar, ranging from 14% to 56% (mean, 22%). For samples with no pretreatment, the CVs were significantly higher than EDHI-treated samples, ranging from 25% to 74% (mean, 39%; 95% confidence interval, 12.10-21.90; p < 0.0001). An intralaboratory comparison of pretreatment regimens confirmed these findings. Some positive antibody specificities present in EDHI-treated samples were negative in corresponding samples with no pretreatment when laboratory-specific cutoffs for positive antibodies were applied. CONCLUSION: Our results show that greater interlaboratory precision can be achieved when samples are pretreated with EDHI as opposed to no pretreatment, likely because these pretreatments eliminate interference from inhibitors. Inhibitors may mask antibodies, leading to missed (or uncalled) specificities when no pretreatment is used.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.245
Teacher spread0.235 · 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 designBench or experimental
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

Citations9
Published2020
Admission routes1
Has abstractyes

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