25 Antiepileptic drug use in children beyond epilepsy: a population-based longitudinal study
Bibliographic record
Abstract
Background Epilepsy is frequently associated with psychiatric disorders and migraine in childhood, suggesting a shared underlying pathological mechanism. Moreover, antiepileptic drugs (AEDs) are also used for treatment of specific psychiatric and pain disorders.Objective To characterize the use of AEDs in children and adolescents beyond epilepsy and seizure disorders.Methods Study population: Children and adolescents (age: 0–19 years) in British Columbia (BC), Canada, with at least one dispensing for an AED between 1997 – 2018. Dispensings were picked up from the pharmacy by patient or caregiver. Data source: BC health administrative databases pharmacy, medical visit and hospitalization data. Design and Analysis: The first AED dispensing was set as the index date. The longitudinal nature of the study allowed the search for diagnostic codes from birth to the index date for children with epilepsy and seizures and those without. The total number of dispensings and all ICD diagnostic codes for all patients were determined across four age ranges (0–4, 5–9, 10–14, 15–19 years). Categorical and continuous variables were analyzed by CMH chi-square and ANOVA methods. Results 6,382 patients (42.6% of all AED users) had at least one dispensing of AEDs in the absence of a diagnosis of epilepsy or seizure disorder.Lamotrigine, valproate and topiramate show increases in the numbers of patients and dispensings in the 5–9 and 10–14 year age categories, with an increase of 45.7% in the patient number for lamotrigine; 12.2% for valproate and 149.1% increase for topiramate. The 2,067 diagnostic codes three months before the first topiramate dispensing in patients 10 - 14 years of age without any evidence for epilepsy or seizure since birth (72.2% of all patients who started topiramate in this age range) included 26.52% for migraine or other type of headaches; 5.1% for mood and bipolar disorder; 13.23% for ADHD and disturbance of conduct, and 6.32% psychosis.Discussion The increase in topiramate usage in the absence of epilepsy was surprising, since its adverse effects on memory, learning and intellectual development are well described. There is a lack of evidence for the use of topiramate for the prevention of migraine in children between 8 and 17 years of age. A previously published placebo-controlled RCT of 361 children with migraine found that topiramate was not more effective than placebo in reducing the number of headache days over 24 weeks.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".