Cannabis for Pediatric Epilepsy
Bibliographic record
Abstract
Epilepsy is a chronic disease characterized by recurrent unprovoked seizures. Up to 30% of children with epilepsy will be refractory to standard anticonvulsant therapy, and those with epileptic encephalopathy can be particularly challenging to treat. The endocannabinoid system can modulate the physiologic processes underlying epileptogenesis. The anticonvulsant properties of several cannabinoids, namely Δ-tetrahydrocannabinol and cannabidiol (CBD), have been demonstrated in both in vitro and in vivo studies. Cannabis-based therapies have been used for millennia to treat a variety of diseases including epilepsy. Several studies have shown that CBD, both in isolation as a pharmaceutical-grade preparation or as part of a CBD-enriched cannabis herbal extract, is beneficial in decreasing seizure frequency in children with treatment-resistant epilepsy. Overall, cannabis herbal extracts appear to provide greater efficacy in decreasing seizure frequency, but the studies assessing cannabis herbal extract are either retrospective or small-scale observational studies. The two large randomized controlled studies assessing the efficacy of pharmaceutical-grade CBD in children with Dravet and Lennox-Gastaut syndromes showed similar efficacy to other anticonvulsants. Lack of data regarding appropriate dosing and pediatric pharmacokinetics continues to make authorization of cannabis-based therapies to children with treatment-resistant epilepsy challenging.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".