P.082 Cannabinoids in the treatment of behavioural symptoms of autism: a rapid review to guide practice
Bibliographic record
Abstract
Background: Legalization of recreational cannabis in Canada has increased the presence of cannabis in the public mind. There are online parent advocacy groups which are already calling for the use of cannabinoids in pediatric developmental and behavioural conditions such as Autism Spectrum Disorder (ASD). We set out to perform a rapid review of existing literature regarding use of cannabinoid products in the treatment of the behavioural domains of ASD. Methods: Key search terms were identified in collaboration with a medical librarian and combined into standardized search filters. A total of 55 articles were identified, of which only two included primary data regarding the use of cannabinoids to control behavioural symptoms of ASD in pediatric populations. Results: Both studies found significant reductions in the behavioural measures examined - which included inappropriate speech, irritability, stereotyped behaviours and self injurious behaviours - after treatment with Cannabinoids. Conclusions: The minimal existing evidence indicates the use of cannabinoid products may be useful in improving behavioural difficulties in children with ASD. However, there is a complete lack of well powered, rigorous studies. Further studies with larger cohorts are needed before any recommendations can be confidently made for or against the use of cannabinoids in this population.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.005 |
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".