EP21.08: Antenatal management of fetal neonatal alloimmune thrombocytopenia (<scp>FNAIT</scp>) and neonatal outcome according to the category of risk
Bibliographic record
Abstract
To retrospectively analyse the management of pregnancies with previous history of FNAIT in terms of neonatal outcome according to the different categories of risk. Pregnancies with a previous FNAIT (Hx FNAIT) between 1993 and 2016 were subdivided in 3 groups: Unknown risk (no alloantibodies, with or without ICH); Standard risk (SR) (maternal alloantibodies without ICH) and High risk (HR) (maternal alloantibodies with ICH). Clinical management (maternal alloantibodies type, medical treatment, intrauterine platelets count and transfusions, IUT) and neonatal outcome (gestational age at delivery, mode of delivery, platelets counts and neurological assessment at birth) were analysed. 58 cases were included in the study subdivided in UR (n= 8, 14%), SR (n=42, 72%) and HR (n= 8, 14%). Table 1 shows the clinical management and neonatal outcome of the three groups. The management of pregnancy with previous FNAIT has to be personalised according to the risk category to achieve good outcome. The combination of medical treatment and fetal platelets assessment and ad hoc platelets transfusions minimise the risk of neonatal thrombocytopenia and ICH. 0.05 (UR) 0.001(SR) 0.05(UR) 0.001(UR & SR) 0.05 (UR) Platelets at birth
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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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".