Maternal <scp>HPA</scp>‐1a antibody level and its role in predicting the severity of Fetal/Neonatal Alloimmune Thrombocytopenia: a systematic review
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 it