Low doses of cannabis extract ameliorate non-motor symptoms of Parkinson’s disease patients: a case series
Bibliographic record
Abstract
Introduction Parkinson’s disease (PD) is mainly characterized by motor symptoms including muscle rigidity, resting tremor and bradykinesia. However, the management of the non-motor symptoms represent a relevant clinical challenger in PD. These non-motor symptoms include cognitive and sleep disturbance and there is evidence that cannabinoids may represent alternative and effective treatments for non-motor symptoms of PD. Methods Therefore, this study addressed the effects of oral treatment with cannabis extract on cognition, insomnia, and daytime sleepiness in six patients with moderate PD. The patients were randomized to receive two different doses of a cannabis extract: THC:CBD 250:28 μg/day (n = 3) or 1000:112 μg/day (n = 3). The assessment of cannabis administration related to the cognitive field was measured by the Montreal Cognitive Assessment test (MoCA test), the insomnia was assessed by the Insomnia Severity Index (ISI), and daytime sleepiness was assessed using the Epworth sleepiness scale (ESS). All clinical evaluations were performed before treatment and at 15, 30, 60, and 90 days of treatment. Results The statistical analysis indicated a significant benefit of the cannabis extract treatment, at dose of 1000:112 μg/day after 60 days of treatment, on insomnia assessed by ISI. Moreover, the statistical analysis of data from ISI and MoCA tests showed a trend toward improvement over time, while no significant effect was observed in the ESS. There were no reports of significant adverse effects during the cannabis extract treatment. Discussion These results demonstrate benefits of short-time treatment (60 days) with low doses of cannabis extract on insomnia in PD patients. This study provide novel findings of the potential of combining CBD and THC as safe and effective treatments for non-motor symptoms of PD.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".