Weak and partial D phenotyping: a comparison study between molecular and serologic results
Bibliographic record
Abstract
Abstract Variant D antigens can cause variable serologic results when typing with Anti-D reagents. There is limited information regarding the ability of Anti-D reagents to differentiate between D variants defined by RHD genotyping. This study was performed to determine if a panel of 20 U.S. Food and Drug Administration–licensed Anti-D reagents can identify molecularly defined D variants. Red blood cells from 119 donors carrying variant RHD alleles were tested at immediate spin (IS) and/or by the indirect antiglobuin test (IAT) using conventional test tube and/or column agglutination technology. Reaction strength at IS and IAT was reviewed to determine whether a pattern of reactivity could be correlated with a specific D variant. Agglutination results from each sample with each Anti-D reagent were combined to assess overall reactivity. The sample set consisted of 21 D variants, based on prior RHD genotyping. Of these variants, nine categories had three or more samples used for analysis ( N = 102); 25 RHD*01W.1, 15 RHD*01W.2, 14 RHD*01W.3, 17 RHD*09.01, 14 RHD*09.03, 4 RHD*01W.4, 23 RHD*07, 4 RHD*10.05, and 6 reference allele RHD*01. As expected, IS showed more negative or weak reactions, and IAT produced more positive reactions with 3+/4+ agglutination strength. RHD*01W.3 samples showed strongest reactivity at IS and IAT. Greatest variation in reactivity was observed with RHD*01W.2, showing weakest overall reactivity at IS. All weak D types had at least one sample that yielded a negative result and one sample with 4+ agglutination at IS. Although there were general patterns of reactivity for each variant tested, no one pattern defined all samples carrying the same RHD allele. This study demonstrated that even with 20 different Anti-D reagents, serologic testing alone is insufficient to define weak or partial D types, characterize the risk for alloanti-D, or determine candidacy for Rh immune globulin. The results illustrate how multiple Anti-D reagents can be used to identify samples that should be reflexed to molecular testing.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".