The effect of serum pretreatment regimens for the detection of HLA class I antibodies in platelet‐refractory patients
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".