Clinical and laboratory predictors of fetal and neonatal alloimmune thrombocytopenia
Bibliographic record
Abstract
Abstract Background Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is the most common cause of intracranial hemorrhage (ICH) in thrombocytopenic term infants. We investigated clinical and laboratory predictors of severe FNAIT in a tertiary care referral center. Study Design and Methods Retrospective cohort study over a 30‐year period. We defined FNAIT as recurrence of neonatal thrombocytopenia in a subsequent pregnancy; and severe outcomes as any of: (1) a birth platelet count below 20 × 109/L; (2) ICH or (3) fetal death. We used a generalized estimating equations analysis and classification tree analysis to identify risk factors for severe FNAIT in a subsequent pregnancy. Results During index pregnancies (n = 135 in 131 mothers), 71 infants (52.6%) had severe outcomes including a platelet count <20 × 109/L (n = 45), fetal or neonatal ICH (n = 32), or fetal death (n = 4). During subsequent pregnancies (n = 72), 15 infants (20.8%) had severe outcomes including birth platelets <20 × 109/L (n = 10), ICH (n = 2), or death (n = 3). Forty‐two women (58.3%) received antenatal intravenous immune globulin (IVIG) during subsequent pregnancies. Eight mothers (n = 9 infants) had severe FNAIT outcomes despite receiving antenatal IVIG. Maternal antibodies to human platelet antigens (HPA) was the only independent predictor of severe FNAIT in a subsequent pregnancy (OR = 25.3, p = .004). Nevertheless, one of 43 infants from antibody‐negative mothers had a severe outcome. Conclusions The presence of anti‐HPA is highly indicative of the diagnosis of severe FNAIT; however, we observed one infant who had severe FNAIT recurrence, defined using strict clinical criteria, without a maternal antibody. Improved diagnostic and therapeutic strategies are needed to prevent severe FNAIT in high‐risk mothers.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".