Medicinal cannabis in children and adolescents with autism spectrum disorder: A scoping review
Bibliographic record
Abstract
BACKGROUND: Autism spectrum disorder (ASD) is a neurodevelopmental condition estimated to affect 1 in 66 children in Canada and 1 in 270 individuals worldwide. As effective therapies for the management of ASD core and associated symptoms are limited, parents are increasingly turning to clinicians for advice regarding the use of medicinal cannabis to manage behavioural disturbances. OBJECTIVE: The objective of this scoping review was to identify and map symptoms, outcomes and adverse events related to medicinal cannabis treatment for ASD-related behaviours. METHODS: Ovid MEDLINE, Embase, CINAHL, PsycInfo, Web of Science Core Collection, Google Scholar and grey literature sources were searched up to 5 January 2020 for studies. Included studies met the following criteria: (1) investigate the use of medicinal cannabis, (2) at least 50% participants had ASD, (3) at least 50% of the study population was 0-18 years old and (4) any study design (published or unpublished). RESULTS: We identified eight completed and five ongoing studies meeting the inclusion criteria. All studies reported substantial behaviour and symptom improvement on medicinal cannabis, with 61% to 93% of subjects showing benefit. In the three studies reporting on concomitant psychotropic medication usage and with cannabis use, up to 80% of participants observed a reduction in concurrent medication use. Adverse events related to cannabis use were reported in up to 27% of participants related, and two participants had psychotic events. CONCLUSIONS: Early reports regarding medicinal cannabis in paediatric ASD symptom management are presented as positive; the evidence, however, is limited to very few retrospective cohort and observational studies. Evidence of safety and efficacy from prospective clinical trials is needed.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".