Cannabinoids in the management of behavioral, psychological, and motor symptoms of neurocognitive disorders: a mixed studies systematic review
Bibliographic record
Abstract
AIM: We undertook this systematic review to determine the efficacy and safety of cannabis-based medicine as a treatment for behavioral, psychological, and motor symptoms associated with neurocognitive disorders. METHODS: We conducted a PRISMA-guided systematic review to identify studies using cannabis-based medicine to treat behavioral, psychological, and motor symptoms among individuals with Alzheimer's disease (AD) dementia, Parkinson's disease (PD), and Huntington's disease (HD). We considered English-language articles providing original data on three or more participants, regardless of design. FINDINGS: We identified 25 studies spanning 1991 to 2021 comprised of 14 controlled trials, 5 pilot studies, 5 observational studies, and 1 case series. In most cases, the cannabinoids tested were dronabinol, whole cannabis, and cannabidiol, and the diagnoses included AD (n = 11), PD (n = 11), and HD (n = 3). Primary outcomes were motor symptoms (e.g., dyskinesia), sleep disturbance, cognition, balance, body weight, and the occurrence of treatment-emergent adverse events. CONCLUSIONS: A narrative summary of the findings from the limited number of studies in the area highlights an apparent association between cannabidiol-based products and relief from motor symptoms in HD and PD and an apparent association between synthetic cannabinoids and relief from behavioral and psychological symptoms of dementia across AD, PD, and HD. These preliminary conclusions could guide using plant-based versus synthetic cannabinoids as safe, alternative treatments for managing neuropsychiatric symptoms in neurocognitive vulnerable patient populations.
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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 | 0.009 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| 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".