Systematic Review of Updates on Pharmacological Management of Recurrent Febrile Convulsions
Bibliographic record
Abstract
Background: Febrile seizures (FS) are the most common convulsive events in early childhood, affecting 2–5 % of children between 6 and 60 months, with up to one-third experiencing recurrence. Although generally benign, recurrent FS cause significant caregiver anxiety and prompt consideration of pharmacological prophylaxis in high-risk cases. Over the last two decades, newer benzodiazepines, second-generation antiseizure medications, and neurohormonal agents have been investigated as alternatives to traditional regimens. Methods: A systematic review was conducted in accordance with PRISMA 2020 guidelines. PubMed, Scopus, and Web of Science were searched for studies published from 1 January 2000 to 30 June 2025 evaluating pharmacological strategies to prevent recurrent FS in children. Eligible designs included randomized controlled trials (RCTs), cohort studies, and systematic reviews reporting recurrence outcomes. Two independent reviewers screened, extracted data, and assessed risk of bias using Cochrane RoB 2 and the Newcastle–Ottawa Scale. Results: Seven studies (n = 577; 3 RCTs, 2 open-label RCTs, 2 cohorts) met inclusion criteria. Intermittent benzodiazepines significantly reduced FS recurrence compared to no prophylaxis. Across three trials, clobazam demonstrated superior efficacy and comparable tolerability to diazepam. Pilot and comparative studies of intermittent levetiracetam (LEV) reported recurrence rates <10 % with fewer behavioral adverse effects relative to clobazam. A single blinded RCT found melatonin non-inferior to diazepam while markedly reducing sedation. No post-2000 evidence supported continuous phenobarbital or valproate prophylaxis. Conclusions: Intermittent clobazam remains the best-supported agent for recurrent FS prevention, while LEV and melatonin are promising, safer alternatives requiring validation in large, multicenter, blinded RCTs. Current evidence supports a selective, individualized approach focused on high-risk children, with caregiver education and rescue strategies as the foundation of management.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".