Cannabis Smoking for Treatment of Parkinson's Disease (PD4.005)
Bibliographic record
Abstract
Objective: To assess the clinical effect of cannabis on the motor and non-motor symptoms of PD. Background Recent years have witnessed a substantial increase in the use of cannabis as a therapeutic agent for various medical conditions. The present study was prompted by several reports of the effect of cannabis on motor symptoms in animal models of Parkinson9s disease. Design/Methods: 17 PD patients (mean age 64.2 years, mean disease duration 7.3 years) currently using cannabis as add on therapy were assessed at the Movement Disorders Clinic. The effect of cannabis on the motor symptoms of the disease was evaluated using the Unified Parkinson9s Disease Rating Scale (UPDRS) at baseline and 30 minutes after smoking Cannabis. The effect of cannabis on non-motor symptoms of PD and its side effects were evaluated using the visual analogue scale (VAS), the present pain intensity scale (PPI) ,the Short-Form Mcgill Pain Questionnaire and the Medical Cannabis Survey National Drug and Alcohol Research Center Questionnaire. Results: There was an overall significant improvement in the mean total motor UPDRS score (33.5 ±14.6 before vs. 22.9 ±11.2 after cannabis smoking; P Conclusions: Cannabis holds promise as another treatment option for PD. It can apparently alleviate not only the motor symptoms but also the non-motor symptoms, especially PD-related pain and sleep, thereby improving patient quality of life. Disclosure: Dr. Lotan has nothing to disclose. Dr. Treves has nothing to disclose. Dr. Roditi has nothing to disclose. Dr. Djaldetti has nothing to disclose.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".