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 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 | 0.006 |
| 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".