Cannabis Treatment in Children with Epilepsy: Practices of Canadian Neurologists
Bibliographic record
Abstract
BACKGROUND: Medical cannabis has recently emerged as a treatment option for children with drug-resistant epilepsy. Despite the fact that many pediatric epilepsy patients across Canada are currently being treated with cannabis, little is known about the attitudes of neurologists toward cannabinoid treatment of children with epilepsy. METHODS: A 21-item online survey was distributed via email to 148 pediatric neurologists working in hospitals and community clinics across Canada. Questions were related to clinical practice and demographics. RESULTS: This survey achieved a response rate of 38% (56 Canadian neurologists). These neurologists were treating 668 pediatric epilepsy patients with cannabinoids. While 29% of neurologists did not support cannabis treatment in their patients, 34% prescribed cannabis, and 38% referred to another authorizing physician, mostly to community-based non-neurologists. The majority of neurologists considered cannabis for patients with Dravet syndrome (68%) and Lennox-Gastaut syndrome (64%) after an average of three failed anticonvulsants. Twenty-seven percent considered it for patients with idiopathic generalized epilepsy, and 18% for focal epilepsy. No neurologist used cannabis as a first-line treatment. All neurologists had at least one hesitation regarding cannabis treatment in pediatric epilepsy. The most common one was poor evidence (66%), followed by poor quality control (52%) and high cost (50%). CONCLUSIONS: The majority of Canadian pediatric neurologists consider using cannabis as a treatment for epilepsy in children. With many gaps in evidence and high patient-driven demand for cannabis therapy, this survey provides immediate information from the "wisdom of the crowd," to aid neurologists until further evidence is available.
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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 | 0.005 |
| 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) | 0.002 | 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".