Exposure to rufinamide and risks of <scp>CNS</scp> adverse events in drug‐resistant epilepsy: a meta‐analysis of randomized, placebo‐controlled trials
Bibliographic record
Abstract
AIM: Epilepsy is a complex disease necessitating continuous development of new therapeutic strategies to encounter drug-resistant cases. Among new adjuvant antiepileptic drugs, rufinamide is structurally distinct from other antiepileptic drugs. It is used to treat partial-onset seizures and seizures associated with Lennox-Gastaut syndrome (LGS) in adult and children. To date, there has been no attempt to evaluate systematically the risks of adverse events with rufinamide. METHODS: We performed a quantitative risk analysis of central nervous system (CNS) adverse events of rufinamide from all randomized, double-blind, add-on, placebo-controlled trials. The meta-analysis was undertaken with fixed effects models. RESULTS: Of the 886 publications reviewed, 99 papers were retrieved and five articles met the inclusion criteria. One thousand two hundred and fifty-two patients were included. Our study showed that exposure to rufinamide was associated with a significant increase in risk of somnolence [relative ratio (RR) 1.87; 95% confidence interval (CI) 1.33, 2.62; P = 0.0003], dizziness (RR 2.66; 95% CI 2.00, 3.55; P = 0.00001), fatigue (RR 2.14; 95% CI 1.57, 2.91; P = 0.01) and headache (RR 1.28; 95% CI 1.02, 1.59, P = 0.03). In addition, exposure to rufinamide was associated with higher treatment discontinuation rates as compared with placebo (RR 2.65; 95% CI 1.74, 4.03; P = 0.00001). CONCLUSIONS: The risk of CNS adverse events appears to be increased in patients exposed to rufinamide as well as the treatment discontinuation rates. However, although statistical associations were significant, additional long term safety studies are required to confirm the clinical significance of these findings, as most reports described only mild and moderate adverse events.
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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 | 0.015 | 0.028 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.048 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".