Fetal/neonatal alloimmune thrombocytopenia: a systematic review of impact of HLA-DRB3*01:01 on fetal/neonatal outcome
Bibliographic record
Abstract
The most common, severe cases of fetal and neonatal alloimmune thrombocytopenia among whites are caused by antibodies against human platelet antigen 1a (HPA-1a). The aims of this systematic review and meta-analysis are to determine the association between maternal HLA-DRB3*01:01 and: (1) HPA-1a-alloimmunization and (2) neonatal outcome in children born of HPA-1a-immunized women. A systematic literature search identified 4 prospective and 8 retrospective studies. Data were combined across studies to estimate pooled odds ratios (ORs) and the associated 95% confidence intervals (CIs). The population represented by the prospective studies was more than 150 000. In the prospective studies, there were 64 severely thrombocytopenic newborns (platelet count <50 × 109/L) of whom 3 had intracranial hemorrhage. The mothers of all 64 children were HLA-DRB3*01:01+. The number of severely thrombocytopenic children born of HPA-1a-alloimmunized women in the retrospective studies was 214; 205 of whom were born of HLA-DRB3*01:01+ women. For HLA-DRB3*01:01- women, the OR (95% CI) for alloimmunization was 0.05 (0.00-0.60), and for severe neonatal thrombocytopenia 0.08 (0.02-0.37). This meta-analysis demonstrates that the risk of alloimmunization and of having a child with severe thrombocytopenia are both very low for HPA-1a- women who are HLA-DRB3*01:01-.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.015 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".