Causes and outcomes of prenatally unexplained fetal anemia
Bibliographic record
Abstract
Despite advances in diagnostic approaches, fetal anemia of unknown etiology continues to be observed in rare cases. This study aimed to assess the incidence of unexplained fetal anemia and to evaluate the associated perinatal outcomes.We conducted an observational retrospective cohort study of all fetuses that underwent fetal blood sampling (FBS) due to an MCA-PSV>1.5 MoM at a tertiary center between 2007 and 2024. Fetuses were included if they had moderate or severe anemia defined as a hemoglobin (Hgb) deviation of more than 20g/L below a gestational age adjusted mean, with a negative anemia workup. Prenatal and postnatal outcomes were obtained.Among 376 fetuses that underwent fetal blood sampling for anemia, 361 (96%) had an identified cause, while 15 (4%) had moderate to severe anemia of unknown etiology. Seven fetuses presented with non-immune hydrops and eight with other major structural anomalies not typically associated with anemia. Eleven (73%) of the 15 fetuses with unexplained anemia had thrombocytopenia, with platelets below 100000/µL in 8 cases and below 50000/µL in 6 cases. Seven cases (47%) resulted in perinatal death. Rare causes of anemia were elucidated in only 5 cases (33%) postnatally despite extensive investigations.Unexplained fetal anemia is uncommon and is associated with poor neonatal outcomes warranting close pre- and postnatal surveillance.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".