Platelets and platelet alloantigens: Lessons from human patients and animal models of fetal and neonatal alloimmune thrombocytopenia
Bibliographic record
Abstract
Platelets play critical roles in hemostasis and thrombosis. Emerging evidence indicates that they are versatile cells and also involved in many other physiological processes and disease states. Fetal and neonatal alloimmune thrombocytopenia (FNAIT) is a life threatening bleeding disorder caused by fetal platelet destruction by maternal alloantibodies developed during pregnancy. Gene polymorphisms cause platelet surface protein incompatibilities between mother and fetus, and ultimately lead to maternal alloimmunization. FNAIT is the most common cause of intracranial hemorrhage in full-term infants and can also lead to intrauterine growth retardation and miscarriage. Proper diagnosis, prevention and treatment of FNAIT is challenging due to insufficient knowledge of the disease and a lack of routine screening as well as its frequent occurrence in first pregnancies. Given the ethical difficulties in performing basic research on human fetuses and neonates, animal models are essential to improve our understanding of the pathogenesis and treatment of FNAIT. The aim of this review is to provide an overview on platelets, hemostasis and thrombocytopenia with a focus on the advancements made in FNAIT by utilizing animal models.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".