A.04 High times? Prevalence and perceptions of marijuana use among patients with epilepsy
Bibliographic record
Abstract
Background: Despite medical advances, almost a third of people with epilepsy have medically refractory epilepsy (MRE). With failure of pharmaceutical options, patients are turning to alternative treatment options. Marijuana use in epilepsy has received extensive attention. Two recent studies evaluated the opinions of marijuana use in individuals with epilepsy, but had discrepant marijuana use rates. Methods: The first 200 adult patients with a known diagnosis of epilepsy seen at Hamilton General Hospital after June 1, 2017 were invited to participate. Standardized paper questionnaires gathered information about demographics, epilepsy history, and marijuana use. Results: One hundred forty participants returned questionnaires; 29.5% were active marijuana users; 24.5% had consumed marijuana in the past. Increased seizure frequency was significantly associated with marijuana use. There was a non-significant trend towards increased marijuana use with males and MRE. Almost half the active marijuana users noted improvement in seizure frequency. No participants experienced worsening of epilepsy with marijuana use. Side effects were common (30%), most frequent being mood. Conclusions: Prevalence of marijuana use among people with epilepsy is higher in our study population compared to an Australian cohort, but similar to Canadian studies. Marijuana use was significantly associated with increased seizure frequency. The majority of patients perceived benefit with regard to seizure control.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".