High‐throughput molecular profiling of blood donors for minor red blood cell and platelet antigens
Bibliographic record
Abstract
BACKGROUND: ABO and D phenotyping of both blood donors and patients receiving transfusions is routinely performed by blood banks to ensure compatibility. These analyses are performed by antibody-based agglutination assays. Blood is not tested for minor blood group antigens on a regular basis, however, because of cost and time constraints. This can result in alloimmunization of the patient against one to several minor antigens and may complicate future transfusions. STUDY DESIGN AND METHODS: To address this problem, an assay has been generated on the GenomeLab SNPstream genotyping system to test simultaneously polymorphisms linked to 22 different blood antigens with donor's DNA isolated from minute amounts of white blood cells. RESULTS: The results showed that both the error rate of the assay, as measured by the strand concordance rate, and the no-call rate were very low (0.1%). The concordance rate with the actual red blood cell (RBC) and platelet (PLT) serology data varied from 97 to 100 percent. Experimental or database errors as well as rare polymorphisms contributing to antigen conformation could explain the observed differences. These rates, however, are well above requirements because phenotyping and cross-matching will always be performed before transfusion. CONCLUSION: Molecular profiling of blood donors for minor RBC and PLT antigens will give blood banks instant access to many different matched donors through the setup of a centralized data storage system.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".