Management of infants born with severe neonatal alloimmune thrombocytopenia: the role of platelet transfusions and intravenous immunoglobulin
Bibliographic record
Abstract
BACKGROUND: Neonatal alloimmune thrombocytopenia (NAIT) is a fetomaternal incompatibility most commonly induced by maternal anti-HPA-1a alloantibodies. Transfusion of immunologically compatible platelets (PLTs) to prevent cerebral hemorrhage, the most severe complication in affected newborns, is usually recommended. Such PLT concentrates, however, are often not readily available. STUDY DESIGN AND METHODS: The efficacy of random-donor PLT transfusions and intravenous immunoglobulin (IVIG) for the management of 17 neonates across four centers with unexpected, severe NAIT was evaluated. Neonates were treated with random-donor PLTs alone (n=7), random-donor PLTs with IVIG (n=8), or matched HPA-1bb PLTs (n=2). RESULTS: All but one patient (treated with random PLTs and IVIG) achieved a posttransfusion PLT count of higher than 30 × 10(9) /L after the first PLT transfusion. The PLT count remained higher than 30 × 10(9) /L for longer than 24 hours in five of seven, seven of eight, and two of four newborns who received random-donor PLTs alone, random-donor PLTs with IVIG, or matched HPA-1bb PLTs, respectively. None of the newborns developed major bleeding or intracranial hemorrhage. IVIG did not appear to improve either posttransfusion PLT counts or total PLT transfusion requirements. CONCLUSION: Transfusion of random-donor PLTs alone was effective at correcting critically low PLT counts and should be considered as first-line treatment of newborns with unexpected severe NAIT.
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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 | 0.000 |
| 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 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".