Blood Group Antigen Matching Influence on Gestational Outcomes (AMIGO) study
Bibliographic record
Abstract
BACKGROUND: Red blood cell (RBC) antigen matching policies to prevent alloimmunization in females of childbearing potential (FCP) vary between centers. To inform transfusion centers responsible for making decisions about matching policies for FCPs, the causal stimulus of the antibodies implicated in severe hemolytic disease of the fetus and newborn (HDFN) must be determined. STUDY DESIGN AND METHODS: We conducted a multinational retrospective study of women with offspring affected by severe HDFN requiring neonatal exchange transfusion and/or intrauterine transfusion. Mothers treated at centers that provide extended antigen-negative RBCs (MATCH, five centers) and those that do not (NoMATCH, nine centers) were compared. RESULTS: A total of 293 mothers had at least one affected pregnancy: 179 at MATCH centers and 114 at NoMATCH centers. Most alloimmunization (83%) was attributed to previous pregnancy: 3% to transfusion (two cases at MATCH, six at NoMATCH centers) and 14% undetermined (both antecedent transfusion and pregnancy). Only 50 mothers had received transfusions; 13 had HDFN due to anti-K at MATCH and four at NoMATCH centers. Most (12/13, 92%) of the anti-K HDFN cases at MATCH centers had K+ paternal antigen status. Mothers at the MATCH centers do not appear to be protected from HDFN due to K, C, c, and E antibodies, although the low number of FCPs who received transfusions precluded drawing firm conclusions. CONCLUSION: The causal stimulus of antibodies that cause HDFN is predominantly from previous pregnancy. Although extended RBC matching for FCPs may impart some protection from allosensitization, we were unable to show a positive effect, possibly because matching policies are not uniform and there was a small number of mothers who previously received transfusions.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".