Rh discrepancies caused by variable reactivity of partial and weak D types with different serologic techniques
Bibliographic record
Abstract
BACKGROUND: RhD discrepancies between current and historical results are problematic to resolve. The investigation of 10 discrepancies is reported here. STUDY DESIGN: Samples identified were those that reacted by automated gel technology and were negative with an FDA-approved reagent. Reactivity with a commercially available panel of monoclonal anti-D was performed. Genomic DNA was evaluated for RHD alleles with multiplex RHD exon polymerase chain reaction (PCR), weak D PCR-restriction fragment length polymorphism, and RHD exon 5 and 7 sequence analyses. RESULTS: The monoclonal anti-D panel identified two samples as DVa, yet possessed the DAR allele. Two weak D Type 1 samples had a similar monoclonal anti-D profile, but only one reacted directly with one of two FDA-approved anti-D. Only two of four weak D Type 2 samples reacted directly with one FDA-approved anti-D, and their D epitope profile differed. CONCLUSIONS: The monoclonal anti-D reagents did not distinguish between partial and weak D Types 1 and 2. Weak D Types 1 and 2 do not show consistent reactivity with FDA-approved reagents and technology. To limit anti-D alloimmunization, it is recommended that samples yielding an immediate-spin tube test cutoff score of not more than 5 (i.e., < or =1+ agglutination) or a score of not more than 8 (i.e., < or =2+ hemagglutination) by gel technology be considered D- for transfusion and Rh immune globulin prophylaxis. That tube test anti-D reagents react poorly with some Weak D Types 1 and 2 red cells is problematic, inasmuch as they should be considered D+ for transfusion and prenatal care. Molecular tests that distinguish common partial and Weak D types provide the solution to resolving D antigen discrepancies.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".