Bibliographic record
Abstract
Blood group genotyping using DNA extracted from fetal tissue is useful to identify fetuses at risk for hemolytic disease of the fetus and newborn (HDFN) due to maternal red cell alloantibodies. Four considerations are important for fetal blood group genotyping. First, paternal heterozygosity must be established, including tests that evaluate RHD hemizygosity. Second, the source of fetal tissue for DNA extraction requires certain considerations. Third, because the fetal genotype is used to predict the expressed phenotype, a thorough knowledge of blood group genetics is required. Moreover, the test algorithm should include the evaluation of the parental phenotypes and genotypes to help identify variant alleles. Fourth, the blood group antigen expression at birth should be evaluated to confirm the inheritance. The identification of an antigen-negative fetus on the basis of the blood group genotype provides significant advantages in managing the pregnancy at risk for HDFN. In the near future, fetal DNA in maternal plasma will likely replace fetal blood group genotyping for RHD. Significant challenges remain to detect other clinically significant blood group antigens using maternal plasma DNA.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.007 |
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".