Cannabinoids for the treatment of neuropsychiatric symptoms, pain and weight loss in dementia
Bibliographic record
Abstract
PURPOSE OF REVIEW: Efficacious treatment for neuropsychiatric symptoms (NPS), pain and weight loss for dementia patients is desperately needed. This review presents an up-to-date look at the literature investigating the use of cannabinoid for these symptoms in dementia. RECENT FINDINGS: We searched electronically for publications regarding cannabinoid use in dementia, with a focus on Alzheimer's disease. Seven studies and one case report have been conducted to examine the use of cannabinoids for the treatment of NPS of dementia, and three of these trials reported on the effect of cannabinoids on weight. Five studies reported decreased agitation or improvements in sleep with cannabinoid use. One crossover trial found that cannabinoids positively impacted weight, whereas a chart review study found no impact on weight with cannabinoids, but an increase in food intake. There were no trials examining the use of cannabinoids for pain in dementia. SUMMARY: Findings from trials with small sample sizes and various clinical populations suggest that cannabinoid use may be well tolerated and effective for treatment of NPS such as agitation as well as weight and pain management in patients with dementia. Additional studies are necessary to further elucidate the relative risks and benefits of this treatment.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".