Refilling and Switching of Antiepileptic Drugs and Seizure-Related Events
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it