Platelet antibodies and fetal growth: maternal antibodies against fetal platelet antigen 1a are strongly associated with reduced birthweight in boys
Bibliographic record
Abstract
OBJECTIVE: To assess whether maternal HPA 1a alloimmunization is associated with birthweight. DESIGN: A retrospective observational cohort study. SETTING: The national reference laboratory for clinical platelet immunology at a university hospital. POPULATION: 165 HPA 1a incompatible pregnancies identified from a recent screening study of 100 448 women (124 pregnancies) and the national reference laboratory for clinical platelet immunology (41 pregnancies). METHODS: A linear mixed model analysis was used to assess whether maternal anti-HPA 1a antibodies were associated with birthweight. A generalized linear model was used to assess maternal anti-HPA 1a antibodies as risk factor for small-for-gestational age neonates. Both models were adjusted for gestational age at time of delivery, maternal age, parity, smoking habits during pregnancy, preeclampsia, diabetes mellitus and fetal sex. MAIN OUTCOME MEASURES. Maternal anti-HPA 1a antibody as risk factor of reduced birthweight and small-for-gestational age neonates. RESULTS: The level of maternal anti-HPA 1a antibodies was significantly associated with birthweight and risk of small-for-gestational age neonates after correcting for confounding variables (p<0.001). However, this association was only significant for boys. When the mother had high levels of anti-HPA 1a antibodies during pregnancy, the adjusted mean birthweight in boys was 530g lower compared with anti-HPA 1a antibody negative pregnancies (p<0.001). CONCLUSIONS: A linear relation between maternal anti-HPA 1a antibody levels and reduced birthweight in boys was demonstrated. Reduced birthweight should be considered a possible complication of fetal and neonatal alloimmune thrombocytopenia.
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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 | 0.002 |
| 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 | 0.001 |
| 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".