Cannabinoids for the Neuropsychiatric Symptoms of Dementia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: In 2016, the global number of individuals living with dementia was 43.8 million, representing a 117% increase from 1990-mainly due to increases in aging and population growth. Up to 90% of individuals with dementia experience neuropsychiatric symptoms (NPS). However, the limitations of current treatments for NPS have drivent he search for safer pharmacotherapies-including cannabinoids. AIM: To assess the efficacy and acceptability of cannabinoids for the treatment of NPS in individuals with dementia. DESIGN: Systematic review and meta-analysis of clinical trials. SETTING AND PARTICIPANTS: = 1). MEASUREMENT: Titles/abstracts were independently screened by one reviewer and reviewed by a second. Full-text screening was by two reviewers with discrepancies resolved via a third reviewer. We extracted data on the standardized mean difference (SMD) for several NPS instruments, trial completion, and adverse events. Data were pooled using random-effects models. FINDINGS: Cannabinoids led to significant improvements across NPS instruments, including the Cohen Mansfield Agitation Inventory (SMD = -0.80; 95% confidence interval [CI], -1.45 to -0.16), the Neuropsychiatric Inventory (SMD = -0.61; CI, -1.07 to -0.15), and nocturnal actigraphy (SMD = -1.05; CI, -1.56 to -0.54h). Cannabinoids were well-tolerated, with an overall trial completion rate of 93% (193/205) and no serious treatment-related adverse events. Treatment efficacy was associated with baseline dementia severity and dose, but not dementia subtype, age, or sex. The overall study quality was rated as low. CONCLUSIONS: There is preliminary evidence for the efficacy and tolerability of cannabinoids as treatments for NPS. Population-based studies are needed to characterize their real-world effectiveness and acceptability.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.030 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".