Intravenous immunoglobulin in the management of severe early onset red blood cell alloimmunisation
Bibliographic record
Abstract
Our objective was to assess the effect of maternal intravenous immunoglobulin (IVIG) administration for severe red blood cell (RBC) alloimmunisation on fetal outcomes. This is a case-control study. Women with a history of severe early onset alloimmunisation resulting in fetal loss in a previous pregnancy and high anti-D or anti-K antibody titres received IVIG in a subsequent pregnancy. We assessed gestational age at first transfusion and fetal outcomes in the subsequent pregnancy and compared these with the outcomes in the previous pregnancy. The most responsible antibody was anti-D in 17 women and anti-K in two others, whilst seven had more than one antibody. In all, 19 women received IVIG in 22 pregnancies, two of which did not even need an intrauterine transfusion (IUT). For previous early losses despite transfusion, IVIG was associated with a relative increase in fetal haemoglobin between treated and untreated pregnancies of 36.5 g/L (95% confidence interval 19.8-53.2, p = 0.0013) and improved perinatal survival (eight of eight vs. none of six, p = 0.001). For previous losses at <20 weeks, it enabled first transfusion deferral in subsequent pregnancies to at least 19.9 weeks (mean 23.2 weeks). Overall, IVIG decreases the severity of haemolytic disease of the fetus and newborn and allows deferral of the first IUT to a safer gestation in severe early-onset RBC alloimmunisation and rarely may even avoid the need for IUT entirely.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 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".