Non-invasive fetal RHD genotyping: A Rapid Qualitative Review [Internet]
Bibliographic record
Abstract
Non-invasive fetal RhD blood group genotyping, also known as fetal RhD genotyping, is meant to identify Rh compatibility between pregnant persons and their fetus. It offers the potential to avoid unnecessary prenatal treatment or other invasive forms of fetal blood group identification.Rh blood group incompatibility occurs most often when a pregnant person’s blood type is RhD negative (RhD−) (i.e., lacks the Rh protein on their red blood cells) and the fetus’ blood type is RhD positive (RhD+). Alloimmunization may occur in Rh incompatible pregnancies when a sufficient amount of the fetus’ RhD+ red blood cells cross into the maternal RhD− blood stream during or after childbirth. Alloimmunization is the development of maternal anti-D antibodies as an immune response to the presence of the fetal blood’s foreign antigens. Once these antibodies have been produced in an Rh incompatible pregnancy, the person and pregnancy are referred to as alloimmunized. While alloimmunization during pregnancy rarely affects the first pregnancy, in subsequent pregnancies the antibodies can pass through the placenta to the fetus, which risks causing hemolysis and hemolytic disease of the fetus and newborn (HDFN).When a pregnant person’s blood type is RhD−, during their first pregnancy they are given prophylactic injections of anti-D Rh-immunoglobulin (RhIG). These injections help prevent alloimmunization and decrease the subsequent risk of developing HDFN (of varying severity). This prophylaxis is given to all RhD− pregnant persons because current standard of care (i.e., routine blood work during pregnancy) is unable to determine the fetus’ RhD status prior to birth. While there are no documented adverse effects of prophylaxis administration in Canada, it is important to note that as a blood product there is always a risk of transmitting infection. Further, as the availability of RhIG is based on the current blood supply (and therefore donations), some healthcare providers have indicated concern regarding its ongoing availability for the RhD− population and advocate thoughtful resource stewardship.
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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 | 0.007 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".