Impact of red blood cell alloimmunization on fetal and neonatal outcomes: A single center cohort study
Bibliographic record
Abstract
BACKGROUND: Alloimmunization can impact both the fetus and neonate. STUDY OBJECTIVES: (a) calculate the incidence of clinically significant RBC isoimmunization during pregnancy, (b) review maternal management and neonatal outcomes, (c) assess the value of prenatal and postnatal serological testing in predicting neonatal outcomes. STUDY DESIGN AND METHODS: A retrospective audit of consecutive alloimmunized pregnancies was conducted. Data collected included demographics, clinical outcomes, and laboratory results. Outcomes included: incidence of alloimmunization; outcomes for neonates with and without the cognate antigen; and sensitivity and specificity of antibody titration testing in predicting hemolytic disease of the fetus and newborn (HDFN). RESULTS: Over 6 years, 128 pregnant women (0.4%) were alloimmunized with 162 alloantibodies; anti-E was the most common alloantibody (51/162; 31%). Intrauterine transfusions (IUTs) were employed in 2 (3%) of 71 mothers of cognate antigen positive (CoAg+) neonates. Of 74 CoAg+ neonates, 58% required observation alone, 23% intensive phototherapy, 9% top up transfusion, and 3% exchange transfusion; no fetal or neonatal deaths occurred. HDFN was diagnosed in 28% (21/74) of neonates; anti-D was the most common cause. The sensitivity and specificity of the critical gel titer >32 in predicting HDFN were 76% and 75%, respectively (negative predictive value 95%; positive predictive value 36%). The sensitivity and specificity of a positive direct antiglobulin test (DAT) in predicting HDFN were 90% and 58%, respectively (NPV 97%; PPV 29%). CONCLUSION: Morbidity and mortality related to HDFN was low; most alloimmunized pregnancies needed minimal intervention. Titers of >32 by gel warrant additional monitoring during pregnancy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".