Establishing assay cutoffs for HLA antibody screening of apheresis donors
Bibliographic record
Abstract
BACKGROUND: Transfusion-related acute lung injury (TRALI) is the leading cause of transfusion-related deaths. Donor HLA antibodies have been implicated in TRALI cases. Blood centers are implementing TRALI risk reduction strategies based on HLA antibody screening of some subpopulations of ever-pregnant apheresis platelet (PLT) donors. However, if screening assay cutoffs are too sensitive, donation loss may adversely impact blood availability. STUDY DESIGN AND METHODS: Pregnancy history and HLA antibody screening and single-antigen bead data from blood donors in the Retrovirus Epidemiology Donor Study-II Leukocyte Antibody Prevalence Study were evaluated for correlations between assay screening values, HLA antibody titer, and number of HLA antigen specificities. The probabilities of matching a cognate antigen in a recipient were calculated and examined in association with total number of specificities observed and screening values. The relative impact of imposing various screening assay cutoffs or pregnancy stratification was examined in relation to detection of HLA antibody-reactive donations and loss of donors and donations. RESULTS: We provide evidence that higher HLA antibody screening assay values are associated with maintaining higher screening signals upon dilution and an increased breadth of specificities compared with lower screening values; the latter correlated with an increased risk of a cognate antigen match in potential recipients. Depending on the TRALI risk reduction strategy used, the potential loss of donations ranged between 0.9 and 6.0%. CONCLUSION: This analysis should enable blood centers to decide upon a TRALI risk reduction strategy for apheresis PLTs that is consistent with how much donation loss the blood center can tolerate.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".