A.05 Ten years of experience with lamotrigine for the treatment of neonatal and infantile seizures
Bibliographic record
Abstract
Background: The therapeutic value of second-generation anticonvulsants such as lamotrigine has not been clearly established in neonates and infants with seizures. To address this issue, we assessed the efficacy of lamotrigine for treating neonatal and infantile seizures, detailed the dosing regimens used, and described its tolerability and safety profile. Methods: This retrospective study included patients (age 0-12 months) diagnosed with seizures and treated with lamotrigine, as monotherapy or adjunctive therapy, by pediatric neurologists at Centre mère-enfant Soleil du CHUQ from 2004 to 2014. The frequency of seizures and EEG patterns were compared before and after introduction of lamotrigine during the first months of life. Data on initial and maintenance doses, rate and magnitude of dosing increments, and adverse effects were collected. Results: Treatment with lamotrigine was initiated in 32 neonates and 13 infants. At first follow-up (mean duration 3 months), 76 % (n = 34) showed a significant (≥50%) reduction of seizures and 64% (n= 29) improvement of EEG pattern compared to baseline. The efficacy in monotherapy and adjunctive therapy was similar. A single case of cutaneous hypersensitivity reaction requiring cessation of treatment was reported. Conclusions: This study suggests that lamotrigine is a useful, safe, and well-tolerated anticonvulsant alternative for the treatment of seizures in neonates and infants.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".