Approach to red blood cell antibody testing during pregnancy: Answers to commonly asked questions.
Bibliographic record
Abstract
OBJECTIVE: To provide family physicians with an understanding of blood bank tests performed during pregnancy. The value of routine blood type and antibody tests, as well as the follow-up required when a patient develops a red blood cell antibody or experiences a fetal-maternal hemorrhage (FMH) will be reviewed. SOURCES OF INFORMATION: The approach described is based on the authors' clinical expertise and peer-reviewed literature from 1967 to 2020. MAIN MESSAGE: An ABO and RhD group and antibody screen test is performed on every pregnant patient during the first trimester. Although antibodies to red blood cell antigens occur infrequently, some can lead to substantial adverse fetal or neonatal consequences including hemolytic disease of the fetus and newborn. Early identification and quantification of important antibodies ensures that at-risk mothers are referred to and followed by obstetricians experienced with high-risk care. Another valuable and related test is the FMH test. For RhD-negative women, these tests are performed at every delivery and following antepartum events that could contribute to FMH. This test determines the number of fetal red blood cells in the maternal circulation and is used to determine the dose of Rh immune globulin an RhD-negative mother requires to prevent alloimmunization to fetal RhD. CONCLUSION: An understanding of blood bank tests performed during pregnancy and their role and limitations is vital to optimal practice and aids clinicians in their decision making. When there is doubt or confusion regarding antenatal testing or immunoprophylaxis, consult the regional laboratory or transfusion medicine specialists for additional guidance.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".