P.043 Cannabis treatment in children with epilepsy: practices and attitudes of neurologists in Canada
Bibliographic record
Abstract
Background: Cannabis has been shown to be an effective therapy for epilepsy in children with Dravet and Lennox-Gastaut syndrome. Despite the fact that many pediatric epilepsy patients across Canada are currently being treated with cannabis, little is known about pediatric neurologists’ attitudes towards it. Methods: A 26-item online survey was distributed to 148 pediatric neurologists across Canada. Results: 56/148 neurologists responded and reported that over 600 children with epilepsy are currently taking cannabinoids. 34% of neurologists authorized cannabis to children, 38% referred children for authorization, and 29% did not authorize or refer their patients. Of those neurologists who referred, 76% referred to a community-based non-neurologist. The majority of physicians authorized cannabis to patients with Dravet syndrome (68%) and Lennox-Gastaut syndrome (64%). Cannabis was never authorized as a first-line treatment. 54% of neurologists stated that their patients were taking CBD alone, despite this option not being available in Canada. All physicians reported having at least one hesitation regarding cannabis, the most common ones being poor evidence (66%), poor quality control (52%), and cost (50%). Conclusions: The majority of Canadian pediatric neurologists use cannabis as a treatment for epilepsy in children. However, there appear to be knowledge gaps and hesitations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".