A practical and effective strategy in East Asia to prevent anti‐D alloimmunization in patients by C/c phenotyping of serologic RhD‐negative blood donors
Bibliographic record
Abstract
Abstract Serologic RhD‐negative red cells can cause anti‐D alloimmunization if they carry the Asian‐type DEL or other DEL variants. RHD genotyping is a viable countermeasure if available, but inexpensive alternatives are worthy of consideration. RhD‐negative blood donors in Japan were studied by anti‐D adsorption‐elution and RHD genotyping. We collated published case reports of RhD‐negative red cell transfusions associated with inexplicable anti‐D immunization. Of 2754 serologic RhD‐negative donors, 378 were genotyped D/d . Anti‐D adsorption‐elution revealed 63.5% (240 of 378) to be DEL, of whom 96.7% (232 of 240) had the 1227G > A variant, diagnostic for the Asian‐type DEL. All 240 donors also carried at least one C antigen; none had a cc phenotype. The chance of transfusing DEL red cells to genuinely RhD‐negative Asian patients (based on a three‐unit transfusion) ranges from 16.7% in Korea to 69.4% in Taiwan, versus 0.6% in Germany. Among 22 RhD‐negative recipients of serologic RhD‐negative red cells, who produced new or increased anti‐D antibody titers, all 17 from East Asia were transfused with red cells with a C‐positive phenotype or known to be Asian‐type DEL or both. Serologic RhD‐negative East Asians with a cc phenotype can be red cell donors for RhD‐negative recipients, especially those of childbearing potential.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".