Beyond neonatal seizures ‐ epileptic evolution in preterm newborns: a systematic review and meta‐analysis
Bibliographic record
Abstract
OBJECTIVE: To assess the potential risk of developing epilepsy in preterm newborns with neonatal seizures (NS). Two electronic databases (PubMed and Web of Sciences) were searched from inception to December 2020. Studies that investigated the outcome of epilepsy in neonates with NS were included. METHODS: Case-control, cross-sectional and cohort studies were included. Data synthesis was undertaken via systematic review and meta-analysis of available evidence. All review stages were conducted by three independent reviewers. We analysed data on neonates with NS who developed post-neonatal epilepsy (PNE) based on the data reported in the selected articles. We then investigated the development of PNE in term and preterm neonates. RESULTS: The initial search led to 568 citations, of which 12 were selected for the review and six were eligible for meta-analysis. Results of the meta-analysis showed no significant difference in the risk of developing PNE between full-term infants with NS (pooled OR [pOR]=0.92: 95% CI: 0.58-1.44) and preterm neonates. SIGNIFICANCE: Gestational age does not seem to be an independent predictor for the development of PNE in neonates with NS. More data are needed to explore the relationship between seizures in the neonatal period and epilepsy later in life.
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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) | 0.014 | 0.023 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".