Management and Neonatal Outcomes of Pregnancies with Fetal/Neonatal Alloimmune Thrombocytopenia: A Single-Center Retrospective Cohort Study
Bibliographic record
Abstract
BACKGROUND: There is no consensus regarding the optimal antenatal treatment of fetal/neonatal alloimmune thrombocytopenia (F/NAIT). We aimed to review the fetal blood sampling (FBS)-related risk, fetal response to maternal intravenous immunoglobulin (IVIG), and cesarean section (CS) rate in pregnancies with a history of F/NAIT. METHODS: Maternal demographics, alloantibodies, pregnancy management, fetal and neonatal outcomes, and index case characteristics were collected. Responders (R) and non-responders (NR) were defined as women treated with IVIG in whom fetal platelets (PLTs) were normal or low (< 50 × 109/L). RESULTS: An FBS-related risk occurred in 1.6% (2/119) of procedures. Maternal characteristics did not differ between responders (n = 21) and non-responders (n = 21). HPA-1a antibody was detected in all non-responders and in 72% of responders (p < 0.01). The index case had a significantly lower PLT count at birth in non-responders versus responders (median PLT count: R = 20 × 109/L [IQR 8-43] vs. NR = 9 × 109/L [IQR 4-18], p < 0.02). No differences were found in IVIG treatment duration or dosage. PLTs at birth were significantly lower in non-responders compared to responders. No intracranial hemorrhages occurred. CSs were performed for obstetric indications only in all but two cases. CONCLUSION: Maternal IVIG can elicit different fetal responses. The lack of prognostic factors to predict responders or non-responders suggests that there remains a role for FBS in F/NAIT in experienced hands.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".