Fetal intracerebral hemorrhage: review of the literature and practice considerations
Bibliographic record
Abstract
Fetal intracerebral hemorrhage is increasingly recognized on prenatal imaging. In this review, we discuss clinically relevant aspects of fetal intracerebral hemorrhage, including germinal matrix-intraventricular hemorrhage, as well as intraparenchymal hemorrhage. We discuss current clinical practice for prenatal counseling and postnatal management of fetal intracerebral hemorrhage, and offer practical recommendations for clinicians. We propose standardized terminology for classification of fetal intracerebral hemorrhage to be used in future research. We also highlight gaps in the literature and priorities for future research, namely the need for prospective large-scale studies to better understand underlying etiologies and neurodevelopmental outcomes in fetal intracerebral hemorrhage. IMPACT STATEMENT: We discuss the diverse etiologies and outcomes of fetal intracerebral hemorrhage, and propose standardized terminology for classification. We outline current practice and offer practical recommendations for management and counseling of fetal intracerebral hemorrhage, recognizing the need for capacity-building in the newly emerging subspecialty of fetal neurology. We highlight gaps in the literature and research priorities in fetal intracerebral hemorrhage to promote collaborative research, and the development of interventions to improve pregnancy and child outcomes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".