P.106 Intravenous lacosamide use in pre-school children
Bibliographic record
Abstract
Background: Data on intravenous lacosamide use in young pediatric patients is scarce, especially of pre-school age. Methods: We retrospectively reviewed the medical records of all patients less than 6 years old who received intravenous lacosamide at our tertiary pediatric hospital. Data on dose, timing and order of administration was collected. Clinical and electrographic response was independently assessed with EEG interpretation blinded to time of administration. For adverse effects surveillance, heart rate was noted before and 1 hour after dose. Results: Eleven patients (8 boys), received lacosamide between 2013 and 2018. Mean age was 2 years (11 days – 5,3 years). Medical indications were: refractory status epilepticus (n=6), repetitive seizures (n=4), and inability to take oral lacosamide (n=1). On average, lacosamide was the fifth (1st-8th) IV antiepileptic drug administered 78 hours (SD 11 hours) after presentation. The most frequent dose was 5 mg/kg. Clinical response was confirmed in 7 patients, while electrographic response was proven in 3 patients. Seizure relapse at 24 hours was noted in 6 patients. No bradycardia occurred post-lacosamide. Conclusions: Although very safe, therapeutic response to lacosamide in young pediatric patients was inconclusive, mostly due to delay in administration, suboptimal dose, and high number of other IV antiepileptic drugs previously given.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".