Maternal <scp>HPA</scp>‐1a antibody level and its role in predicting the severity of Fetal/Neonatal Alloimmune Thrombocytopenia: a systematic review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: In Caucasians, fetal/neonatal alloimmune thrombocytopenia (FNAIT) is most commonly due to maternal HPA-1a antibodies. HPA-1a typing followed by screening for anti-HPA-1a antibodies in HPA-1bb women may identify first pregnancies at risk. Our goal was to review results from previous published studies to examine whether the maternal antibody level to HPA-1a could be used to identify high-risk pregnancies. MATERIALS AND METHODS: The studies included were categorized by recruitment strategies: screening of unselected pregnancies or samples analyzed from known or suspected FNAIT patients. RESULTS: Three prospective studies reported results from screening programmes, and 10 retrospective studies focused on suspected cases of FNAIT. In 8 studies samples for antibody measurement, performed by the monoclonal antibody immobilization of platelet antigen (MAIPA) assay, and samples for determining fetal/neonatal platelet count were collected simultaneously. In these 8 studies, the maternal antibody level correlated with the risk of severe thrombocytopenia. The prospective studies reported high negative predictive values (88-95%), which would allow for the use of maternal anti-HPA-1a antibody level as a predictive tool in a screening setting, in order to identify cases at low risk for FNAIT. However, due to low positive predictive values reported in prospective as well as retrospective studies (54-97%), the maternal antibody level is less suited for the final diagnosis and for guiding antenatal treatment. CONCLUSION: HPA-1a antibody level has the potential to predict the severity of FNAIT.
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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 | 0.003 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.006 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".