2234 Perampanel for the treatment of paediatric patients in clinical practice by age category
Bibliographic record
Abstract
Objectives To assess perampanel (PER) in everyday clinical practice in paediatric patients. Methods Paediatric patients treated with PER were identified from a pooled analysis of 44 global studies. Retention was assessed after 3, 6 and 12 months. Responder rate (≥50% seizure frequency reduction), seizure freedom rate (no seizures since at least prior visit) and adverse events (AEs) were evaluated. Data were analysed by age category. Results 56 patients were identified (<4 years, n=5; 4–<7 years; n=12; 7–<12 years, n=39). Mean PER doses at baseline and last visit (last observation carried forward) were 1.8 and 2.3 mg/day; 1.8 and 4.1 mg/day; and 2.1 and 4.9 mg/day, in the three respective groups. Retention rates at 3, 6 and 12 months in patients aged <4 years were 50.0% at all timepoints; corresponding rates for patients aged 4–<7 and 7–<12 years were 90.0%, 70.0% and 0%, and 89.7%, 76.9% and 63.6%. At last visit, responder and seizure freedom rates in patients aged <4 years were 66.7% and 33.3%; corresponding rates for patients aged 4–<7 and 7–<12 years were 36.4% and 9.1%, and 58.3% and 27.8%. AEs were reported for 0%, 40.0% and 38.2% in the three groups. Most frequently reported were irritability (<4 years, 0%; 4–<7 years, 10%; 7–<12 years, 14.7%) and dizziness/vertigo (<4 years, 0%; 4–<7 years, 0%; 7–<12 years, 8.8%). Conclusions PER was effective and generally well tolerated in this small population of paediatric patients treated in clinical practice. Supported by Eisai
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".