Refilling and Switching of Antiepileptic Drugs and Seizure-Related Events
Bibliographic record
Abstract
We sought to estimate the risk of seizure-related events associated with refilling prescriptions for antiepileptic drugs (AEDs) and to estimate the effect of switching between brand-name and generic drugs or between two generic versions of the same drug. We conducted a case-crossover study using health-care databases from British Columbia, Canada, among AED users who had an emergency room visit or hospitalization for seizure (index seizure-related event), defined using International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9) codes 345.xx (epilepsy and recurrent seizures) and 780.3x (convulsions), between 1997 and 2005. AED prescription refilling itself was associated with 2.3-fold elevated odds of seizure-related events when the refill occurred within 21 days before the index event (odds ratio (OR) 2.31; 95% confidence interval (CI) 1.56-3.44). The OR was 2.75 (95% CI 0.88-8.64) for refills that involved switching, yielding a refill-adjusted OR for switching of 1.19 (95% CI 0.35-3.99). Refilling the same AED prescription was associated with an elevated risk of seizure-related events whether or not the refill involved switching from a brand-name to a generic product.
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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 | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".