Effect of medical cannabis on thermal quantitative measurements of pain in patients with Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Cannabis can alleviate pain of various etiologies. This study assessed the effect of cannabis on motor symptoms and pain parameters in patients with Parkinson's disease (PD). METHODS: Twenty patients with PD who were licensed to use cannabis underwent evaluation before and 30 min after cannabis consumption and again after long-term use. Motor function was assessed with the Unified PD Rating scale (UPDRS) by two raters, one blinded. Pain was assessed with the Pain Rating Index (PRI) and Visual Analogue Scale (VAS) of the short-form McGill Pain Questionnaire. Thermal quantitative sensory testing (QST) was performed in 18 patients. The two consecutive QST measurements were validated in 12 cannabis-naïve patients with PD. RESULTS: There was a significant decrease from baseline to 30 min after cannabis consumption in mean motor UPDRS score (38.1 ± 18 to 30.4 ± 15.6, p < 0.0001), total PRI (27 ± 13.5 to 9.7 ± 11, p = 0.001), and VAS score (6.4 ± 2.8 to 3.6 ± 3.1, p = 0.0005). Mean cold pain threshold decreased significantly in the more affected limb, but only after exclusion of two patients who consumed cannabis by vaporizer rather than smoking (19.5 ± 5.2 to 15.6 ± 8.7 °C, p = 0.02). After long-term (median 14 weeks) exposure, mean heat pain threshold decreased significantly in the more affected limb in all treated patients (43.6 ± 3.5 to 40.9 ± 3.3 °C, p = 0.05) and in cannabis smokers (43.7 ± 3.6 to 40.3 ± 2.5 °C, p = 0.008). CONCLUSIONS: Cannabis improved motor scores and pain symptoms in PD patients, together with a dissociate effect on heat and cold pain thresholds. Peripheral and central pathways are probably modulated by cannabis. SIGNIFICANCE: Quantitative sensory test results are significantly altered following cannabis consumption in patients with PD. Cannabis probably acts on pain in PD via peripheral and central pathways.
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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 | 0.002 |
| 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) | 0.001 | 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".