Medical Marijuana Effects in Movement Disorders, Focus on Huntington Disease; A Literature Review
Bibliographic record
Abstract
PURPOSE: We aimed to comprehensively evaluate the effects of medical marijuana on symptoms that are relevant to movement disorders with a focus on Huntington disease (HD). METHODS: A systematic review by literature search through PubMed and EBSCO electronic databases was conducted for relevant studies reported after 2002 on the effects of medical marijuana or cannabis use on tremor, spasm, spasticity, chorea, sleep quality and HD-specific rating scales. Study selection, quality assessment and data extraction was performed by three reviewers. Outcome measures were changes in psychomotor, and sleep related symptoms. The methodological quality of the included studies was evaluated. Results: A total of 22 studies were reviewed. There was strong evidence for significant improvement in the neurologic symptoms of spasms, tremors, spasticity, chorea, and quality of sleep following treatment with medical marijuana. Analysis of specific motor symptoms revealed significant improvement after treatment in tremors and rigidity. Furthermore, all pretreatment and post-treatment measures indicated a significant increase in average number of hours slept. CONCLUSION: Larger scale studies are warranted to test the benefits of medical marijuana in HD patients. In the meanwhile, clinicians may consider prescribing medical marijuana as part of their strategy for better symptomatic treatment of patients with HD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".