Resolving variable maternal D typing using serology and genotyping in selected prenatal patients
Bibliographic record
Abstract
BACKGROUND: RhIG prophylaxis for D- pregnant women prevents hemolytic disease of the newborn and typically depends on results of serologic D typing. Interpretation and follow-up of weak D serology is variable. Recent recommendations promote genotyping for RHD status determination in those with weak D serology. Canadian Blood Services performs comprehensive serologic prenatal testing in four provinces. Genotyping is used to determine D typing in patients with weak D. STUDY DESIGN AND METHODS: A serologic algorithm identified which patients require genotyping for RHD determination. Genotyping was performed on one of two commercially available platforms. RESULTS: Only 0.4% of D- patients met criteria for genotyping. Sixty-one percent were weak D Type 1, 2, or 3. Thirty percent had a partial or weak D other than Type 1, 2, or 3. Eleven had variants which remained unresolved. Seventeen were D+ and four were D-. CONCLUSIONS: Genotyping of patients with weak D serology led to an identified genotype in most patients. RhIG administration was avoided in 66% who were weak D Type 1, 2, or 3 or were D+. The use of a serologic algorithm to select patients for RHD genotyping identifies a majority of patients with weak D types not at risk for alloimmunization. This approach limits the number of genotyping investigations and the cost of providing classification for weak D types.
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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".