Identification of <i>RHD</i> alleles with the potential of anti‐D immunization among seemingly D− blood donors in Upper Austria
Bibliographic record
Abstract
BACKGROUND: Aberrant RHD alleles leading to a reduced expression of D antigen on the red blood cell (RBC) surface may be mistyped as D- by serology. To quantify the occurrence of weak D, DEL, and D+/- chimera among apparent D- first-time blood donors, polymerase chain reaction (PCR) screening was implemented as a routine service. STUDY DESIGN AND METHODS: A total of 23,330 pretyped D- samples were tested for RHD markers in Exons 4, 7, and 10 in pools of 20 by PCR. Samples with positive results in PCR were reevaluated by exon-specific PCRs, DNA sequencing, and serologic methods. RESULTS: Among 94 PCR-positive samples, 74 exhibited a weak D or DEL phenotype, dubbed weak D type 1, weak D type 2, weak D type 5, weak D type 32, weak D type 4.3, RHD(M295I), RHD(del147), and RHD(1227G>A). The most prevalent alleles were weak D type 4.3 (n = 31) and RHD(IVS3+1G>A) (n = 24). CONCLUSIONS: As a clinical consequence, 74 blood donor samples carrying weak D and DEL phenotypes with the potential of causing secondary immunizations in recipients were reclassified as D+. Those samples were reliably amplified by RHD Exon 7 PCR; therefore, its usage in the Upper Austrian population is recommended. The association of the weak D type 4.3 samples with a ce leads to the policy that all apparently D- donors should be tested with genotyping methods; otherwise, potentially immunogenic RHD alleles may be overseen.
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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 | 0.001 | 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) | 0.001 | 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".