The impact of methylphenidate on seizure frequency and severity in children with attention‐deficit–hyperactivity disorder and difficult‐to‐treat epilepsies
Bibliographic record
Abstract
AIM: Difficult-to-treat epilepsies and attention-deficit-hyperactivity disorder (ADHD) often co-occur. Because of concerns about the use of stimulants in children with this comorbidity, the impact of ADHD treatment on seizure frequency and severity is not known. This pilot study evaluated the safety and efficacy of methylphenidate in this population. METHOD: After a 3 month period in which antiepileptic drugs were adjusted, 22 patients recruited from a specialist outpatient clinic for severe epilepsy (16 males, six females; mean age 11 y 2 mo, SD 3 y 2 mo) received methylphenidate for 3 months in an open label, non-controlled trial; four with generalized or multifocal (symptomatic/cryptogenic) epilepsy, one with generalized (idiopathic) epilepsy, 17 with partial (symptomatic/cryptogenic) epilepsy; five with partial seizures only, 17 with primarily or secondarily generalized seizures). Epilepsy, ADHD symptoms, and side effects were assessed using the Swanson, Nolan, and Pelham Questionnaire, the Child Behavior Checklist, the Hague Seizure Severity Scale, and the Side Effects Rating Scale. RESULTS: Methylphenidate significantly improved ADHD. After 3 months of treatment, 73% of patients no longer had clinically significant symptoms. Methylphenidate also reduced seizure severity (9-point median decrease on the Hague Seizure Severity Scale). Seizure frequency increased in four out of 22 patients, but only one patient withdrew from the study for this reason. Most patients experienced no major side effects. INTERPRETATION: These data are among the first showing that low doses of methylphenidate are safe and effective to treat ADHD symptoms in patients with difficult-to-treat epilepsies. Randomized controlled trials are needed to replicate the findings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".