P4‐003: Designing a Randomized Placebo‐Controlled Crossover Trial Investigating Nabilone as a Treatment for Agitation in Patients with Moderate‐to‐Severe Alzheimer’s Disease
Bibliographic record
Abstract
While current pharmacological recommendations for the management of agitation in AD call for the judicious use of antipsychotics, these medications have modest benefits and have been associated with high risk-profiles. Nabilone, a synthetic cannabinoid, has a distinct pharmacological profile which may provide a safer and more effective alternative to treat agitation, while potentially having benefits for weight and pain. Additionally, emerging evidence has shown nabilone to have neuroprotective and anti-inflammatory effects, which can reduce oxidative stress and brain cholesterol metabolism. We describe a clinical trial to investigate the safety and efficacy of nabilone in the treatment of agitation, as well as pain and weight loss, in patients with moderate-to-severe AD. This will be a double-blind, crossover randomized placebo controlled trial (RCT) comparing 6 weeks of nabilone to 6 weeks of placebo, with a 1-week washout preceding each treatment phase. Eligible patients with moderate-to-severe AD and clinically significant agitation will be randomized to receive either nabilone (.5 – 2 mg) or placebo (1:1 ratio) for 6 weeks each. The recruitment goal is to randomize 40 patients over 2 years. The primary outcome will be agitation, as measured by the Cohen-Mansfield Agitation Inventory. The secondary outcomes will include overall behaviour, cognition and global impression. Exploratory outcomes include pain, nutritional status, safety and biomarkers of oxidative/nitrosative stress, inflammation and cholesterol metabolism. This crossover RCT will establish the therapeutic relevance of nabilone in the treatment of AD by examining its efficacy in the treatment of agitation, pain and weight, while collecting double-blind information on safety. If positive, the findings of this study will provide rationale for the feasibility of a larger, multicentre trial. Additionally, a safe and efficacious pharmacological intervention for agitation, pain and weight loss in patients with moderate-to-severe AD could increase quality-of-life, reduce caregiver stress and avoid unnecessary institutionalization and related increases in health care costs.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".