Cannabis and its derivatives for the use of motor symptoms in Parkinson’s disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Background: Recent changes to the legal status of cannabis across various countries have renewed interest in exploring its use in Parkinson’s disease (PD). The use of cannabinoids for alleviation of motor symptoms has been extensively explored in pre-clinical studies. Objective: We aim to systematically review and meta-analyze literature on the use of medical cannabis or its derivatives (MC) in PD patients to determine its effect on motor function and its safety profile. Methods: We reviewed and analyzed original, full-text randomized controlled trials (RCTs) and observational studies. Primary outcomes were change in motor function and dyskinesia. Secondary outcomes included adverse events and side effects. All studies were analyzed for risk of bias. Results: Fifteen studies, including six RCTs, were analyzed. Of these, 12/15 (80%) mention concomitant treatment with antiparkinsonian medications, most commonly levodopa. Primary outcomes were most often measured using the Unified Parkinson Disease Rating Scale (UPDRS) among RCTs and patient self-report of symptom improvement was widely used among observational studies. Most of the observational data lacking appropriate controls had effect estimates favoring the intervention. However, the controlled studies demonstrated no significant motor symptom improvement overall. The meta-analysis of three RCTs, including a total of 83 patients, did not demonstrate a statistically significant improvement in UPDRS III score variation (MD −0.21, 95% CI −4.15 to 3.72; p = 0.92) with MC use. Only one study reported statistically significant improvement in dyskinesia ( p < 0.05). The intervention was generally well tolerated. All RCTs had a high risk of bias. Conclusion: Although observational studies establish subjective symptom alleviation and interest in MC among PD patients, there is insufficient evidence to support its integration into clinical practice for motor symptom treatment. This is primarily due to lack of good quality data.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".