Antenatal diagnosis of fetal intraventricular hemorrhage: systematic review and meta‐analysis
Bibliographic record
Abstract
AIM: To determine how the severity of antenatally diagnosed germinal matrix-intraventricular hemorrhage (GMH-IVH) relates to morbidity and mortality, and to explore potential risk factors. METHOD: We conducted a systematic review and individual patient data meta-analysis of antenatally diagnosed fetal GMH-IVH. The primary outcomes were mortality and morbidity. Potential associations with clinical factors during pregnancy were explored. Analysis employed Fisher's exact test and logistic regression. RESULTS: We included 240 cases from 80 studies. Presence of venous infarction was associated with mortality (odds ratio [OR] 4.3, 95% confidence interval [CI] 1.4-13.25), motor impairment (OR 103.2, 95% CI 8.6-1238), epilepsy (OR 6.46, 95% CI 2.64-16.06), and developmental delay (OR 8.55, 95% CI 2.12-48.79). Shunt placement was associated with gestational age at GMH-IVH diagnosis and in utero progression. Many cases had uncomplicated pregnancies but possible co-occurring conditions included twin gestation, small for gestational age, and congenital anomalies. INTERPRETATION: Severity of fetal GMH-IVH, specifically venous infarction, is associated with overall mortality and morbidity. Risk factors for fetal GMH-IVH are poorly understood and controlled studies are required. WHAT THIS PAPER ADDS: Preterm germinal matrix-intraventricular hemorrhage (GMH-IVH) grading can be applied to fetuses. Many fetal germinal matrix hemorrhages occur in otherwise typical pregnancies. Half of fetuses with post-hemorrhagic ventricular dilatation receive a shunt after delivery. Fetuses with grade I or II GMH-IVH have few sequelae. Fetuses with periventricular hemorrhagic infarction have a high burden of motor impairment.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.022 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".