Partial D, weak D types, and novel<i>RHD</i>alleles among 33,864 multiethnic patients: implications for anti‐D alloimmunization and prevention
Bibliographic record
Abstract
BACKGROUND: The D antigen includes category D, partial D, and weak D types, which are important because anti-D alloimmunization can occur in some but not all persons that express a variant RHD allele. At present, there is little prospective information on the prevalence of D variants among obstetric patients and potential transfusion recipients. STUDY DESIGN AND METHODS: The RHD alleles were prospectively examined in a large patient population identified on the basis of a difference in anti-D reactivity between two reagents. RESULTS: Fifty-five discrepancies (0.96% of D-) were noted among 33,864 ethnically diverse patients over 18 months, of which 54 represented mutated RHD alleles. Seven obstetric patients were assigned D- status based on serology; only 1 patient had a partial RHD allele. Ten of 25 (36%) obstetric patients and 4 of 6 (67%) female potential transfusion recipients of childbearing age or younger were assigned D+ status, and they expressed a D variant known to permit anti-D alloimmunization. In total 20 RHD alleles were identified including category, DVa or DVa-like alleles (n = 7), DAR (n = 8), and four novel RHD alleles including two new DAU alleles. CONCLUSION: Given the complexity of D antigen expression, it is concluded that some clinically important D variants identified by standard serologic analysis phenotype as D+ and are potentially at risk for the development of anti-D.
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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 | 0.000 | 0.002 |
| 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".