Efficacy of antiepileptic drugs in neonatal seizures: a systematic review protocol
Bibliographic record
Abstract
Introduction Seizures are one of the most common neurological disorders of neonates, which is also an emergency in the neonatal intensive care unit. For neonates, the recommended first-line antiepileptic drugs (AEDs) include phenobarbitone, which may be effective in only 50% of seizures. Some new AEDs, such as levetiracetam, have been shown to be effective in adults and older children. However, their efficacy for neonatal seizures remains uncertain. The aim of this investigation is to conduct a systematic review to evaluate the efficacy of all AEDs in neonates. Additionally, the long-term outcomes following neonatal seizures, in relation to the development of cerebral palsy and epilepsy, will be studied. Method We will perform a systematic review including randomised controlled studies (RCTs), cohort studies, case-controlled studies and case series studies which evaluated the efficacy of AEDs and short-term and long-term outcomes in neonatal seizures. PubMed, Embase, Web of Science, Cochrane Library and Clinical trial.gov will be searched. There will be no language restriction. Risk bias in RCTs will be evaluated by the Cochrane risk-of-bias tool, while cohort and case-control studies will be evaluated by the Newcastle-Ottawa Scale. A network meta-analysis will be performed by the Bayesian model using WinBUGS V.1.4.3 and R software if there is a high degree of homogeneity among studies. Otherwise, we will perform a narrative review without pooling. Subgroup analyses will be performed in different AEDs and dosage groups. Outcome The primary outcomes will be seizure cessation confirmed by electroencephalogram and long-term neurodevelopmental outcome. Secondary outcomes will be neonatal mortality during hospitalisation and suspected drug toxicity. Ethics and dissemination Formal ethical approval is not required as no primary data are collected. This systematic review will be disseminated through a peer-reviewed publication.
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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 | 0.047 | 0.045 |
| Meta-epidemiology (narrow) | 0.006 | 0.006 |
| Meta-epidemiology (broad) | 0.021 | 0.015 |
| Bibliometrics | 0.014 | 0.012 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.006 | 0.005 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.084 | 0.009 |
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".