P.048 Prevalence, type and risk factors of intracranial hemorrhage in term neonates: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Intracranial hemorrhage (ICH) in newborns poses a significant challenge to wellbeing and development. In preterm neonates, germinal matrix hemorrhage is most common. In term neonates, prevalence and type of ICH has not been well elucidated. This systematic review aims to assess prevalence, type, and risk factors of ICH in term neonates. Methods: A systematic review was conducted. Inclusion criteria was ICH in neonates born at 37+ weeks gestation. Exclusion criteria was one type of ICH, one risk factor, sample size <20, text not in English, full text not accessible. Eligible studies were evaluated by two authors, data was extracted and analyzed using a predesigned template and MetaXL. Results: A total of 1226 records were initially identified and 20 studies were included in the final analysis. The overall prevalence of ICH was 9.3%. This was subdivided into an asymptomatic subgroup (5.8%) and symptomatic subgroup (29.3%). Analysis showed CT detected ICH most commonly. Extra-axial hemorrhage was most commonly detected (~30%), with subdural more common than extradural hemorrhages. The odds of having an ICH was significantly higher with instrumental delivery (3.75%). Conclusions: This shows that prevalence of ICH is relatively high in symptomatic children. Measured prevalence varies according to the type of modality used for screening.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".