A prospective open‐label trial of a <scp>CBD</scp>/<scp>THC</scp> cannabis oil in dravet syndrome
Bibliographic record
Abstract
Abstract Introduction Both Δ 9 Tetrahydrocannabidiol ( THC ) and cannabidiol ( CBD ) components of cannabis, have been shown to have anticonvulsant effects. Cannabis oils are used to treat seizures in drug‐resistant epilepsy ( DRE ). Recent trials provide data on dosing, side effects, and efficacy of CBD , yet there is a paucity of information on THC in epilepsy. Primary objective was to establish dosing and tolerability of TIL ‐ TC 150 ‐ a cannabis plant extract produced by Tilray ® , containing 100 mg/mL CBD and 2 mg/mL THC ‐ in children with Dravet syndrome. Secondary objectives were to assess impact of therapy on seizures, electroencephalogram ( EEG ) and quality of life. Methods Twenty children received add‐on therapy with TIL ‐ TC 150. The dose ranged from 2 to 16 mg/kg/day of CBD and 0.04 to 0.32 mg/kg/day of THC . Patients were monitored for tolerability and adverse events, and secondary objectives. Results Nineteen participants completed the 20‐week intervention. Mean dose achieved was 13.3 mg/kg/day of CBD (range 7–16 mg/kg/day) and 0.27 mg/kg/day of THC (range 0.14–0.32 mg/kg/day). Adverse events, common during titration included somnolence, anorexia, and diarrhea. Abnormalities of liver transaminases and platelets were observed with concomitant valproic acid therapy. There was a statistically significant improvement in quality of life, reduction in EEG spike activity, and median motor seizure reduction of 70.6%, with 50% responder rate of 63%. Conclusions TIL ‐ TC 150 was safe and well tolerated in our subjects. TIL ‐ TC 150 treatment resulted in a reduction in seizure counts, spike index on EEG , and improved quality of life measures. This study provides safety and dosing information for THC ‐containing cannabinoid preparations.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".