Natural and Synthetic Cannabinoids for Agitation and Aggression in Alzheimer’s Disease
Bibliographic record
Abstract
OBJECTIVE: This meta-analysis investigated the efficacy of cannabinoids on agitation and aggression in patients with Alzheimer's disease (AD). DATA SOURCES: Electronic records up to August 2018 were searched from MEDLINE, EMBASE, and PsycINFO. Search terms included Alzheimer's disease, agitation, aggression, and cannabinoids. STUDY SELECTION: Double-blind, placebo-controlled studies investigating the effect of cannabinoids on agitation in patients with AD were included. Of the 1,336 records returned, 123 were reviewed and 6 (N = 251 participants) were included. DATA EXTRACTION: Data on demographics, study setting, trial length, intervention, outcomes, and dropouts were extracted. RESULTS: There was no effect of cannabinoids as a group on agitation (standard mean difference: -0.69, P = .10), though there was significant heterogeneity (χ²₆ = 43.53, P < .00001, I² = 86%). There was a trend for greater difference in agitation with synthetic cannabinoids over tetrahydrocannabinol (χ²₁ = 3.05, P = .08). Cannabinoids had a larger effect on agitation with greater cognitive impairment (B = 0.27, t₆ = 2.93, P = .03). Cannabinoids did not change overall neuropsychiatric symptoms or body mass index (BMI). However, there was a significant difference in patients with a lower BMI compared to patients with a higher BMI (χ²₁ = 4.63, P = .03). Sedation was significantly greater with cannabinoids compared to placebo (risk ratio = 1.73, P = .04), but there were no differences in the occurrence of adverse events or dropouts due to an adverse event between treatment groups. CONCLUSIONS: The efficacy of cannabinoids on agitation and aggression in patients with AD remains inconclusive, though there may be a signal for a potential benefit of synthetic cannabinoids. Safety should be closely monitored as cannabinoid treatment was associated with increased sedation.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.031 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".