Management of alloimmune thrombocytopenia
Bibliographic record
Abstract
Fetal alloimmune thrombocytopenia is caused by maternal sensitization to paternally-derived antigens on fetal platelets, most commonly HPA-1a.1 It occurs in approximately 1 in 1000 live births and is the commonest cause of severe fetal and neonatal thrombocytopenia, and of intracranial hemorrhage in neonates born at term.2 Since there is currently no routine screening, first-time cases of fetal alloimmune thrombocytopenia are generally identified following the birth of a markedly thrombocytopenic neonate. Antenatal management is thus only possible in subsequent pregnancies. \n \nIntracranial hemorrhage is the most devastating complication of fetal alloimmune thrombocytopenia and often occurs antenatally. Assessment of projected clinical severity is thus based on the development of intracranial hemorrhage in a previous sibling. If there is such a history of intracranial hemorrhage, the chance of this complication occurring again in the next pregnancy is extremely high in an untreated, antigen-positive sibling.3 \n \nAdministration of intravenous immunoglobulin (IVIG) to the mother, initially given in conjunction with dexamethasone, was first used to prevent recurrence of antenatal intracranial hemorrhage in 1988.4 This approach of providing IVIG-based medical therapy administered to the mother to increase the fetal platelet count has since been extensively investigated in hundreds of maternal-fetal pairs.5 The efficacy of IVIG-based therapy has been supported by numerous studies6–16 (Table 1A) but not by others17–19 (Table 1B). The studies presented in Tables 1A and 1B surprisingly report virtually identical percentages of cases of intracranial hemorrhage: 2.7% versus 2.9%, respectively. However, overall mean birth platelet counts differed markedly between the two groups. While platelet counts are considered to be surrogate markers of intracranial hemorrhage, fortunately, the likelihood of fetal and neonatal intracranial hemorrhage, in the absence of this complication having occurred in a previous sibling, is relatively low.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".