Medicinal Marijuana for Epilepsy: A Case Series Study
Bibliographic record
Abstract
OBJECTIVE: To describe the social, clinical and use-patterns characteristics of medicinal marijuana use among patients with epilepsy (PWEs). METHODS: Eighteen PWEs with prescriptions for medicinal marijuana from a Canadian adult-epilepsy clinic were included in this study. RESULTS: Eighteen patients had a prescription of medicinal marijuana from a total population of 800 PWEs in our center (2.2%). Mean age of patients was 30±7.4 (19-50) years. Twelve (67%) patients were males. Eleven (61%) patients had drug-resistant epilepsy. Eleven (61%) patients suffered a psychiatric comorbidity and reported the use of illicit substances or heavy alcohol or tobacco consumption. Only two (11%) patients were married; the rest of patients (89%) were single or divorced. The drug use pattern was similar among patients. All patients asked for marijuana permission in the epilepsy clinic. Most (83%) had a previous history of marijuana smoking, with a mean of 6.6±3 (1-15) years. The mean consumption dose was 2.05±1.8 (0.5-8) grams per day. Ten (56%) patients reported withdrawal seizure exacerbation when they stopped the marijuana. Only two patients (11%) reported side effects, and all patients found medicinal marijuana very helpful for seizure control and improvement of mood disorder. CONCLUSIONS: PWEs using medicinal marijuana have a common profile. They are usually young single men with drug-resistant epilepsy and psychiatric comorbidity. Most used marijuana before formal prescription and all believe the drug was effective on their seizure control. Because of the concurrent use of other antiseizure medications, it is complex to estimate the actual effect of marijuana.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".