UK Medical Cannabis Registry: A Clinical Outcomes Analysis for Complex Regional Pain Syndrome
Bibliographic record
Abstract
BACKGROUND: Complex regional pain syndrome is characterized by severe, persistent pain. Emerging evidence suggests that cannabis-based medicinal products may represent a new therapeutic option. However, to date, no clinical studies have evaluated the effects of cannabis-based medicinal products in individuals with complex regional pain syndrome. The aim of this study is to assess changes in patient-reported outcome measures and the prevalence of adverse events associated with cannabis-based medicinal products prescribed for complex regional pain syndrome. METHODS: This case series assessed changes in patient-reported outcome measures over 6 months in complex regional pain syndrome patients enrolled in the UK Medical Cannabis Registry. Adverse events were measured and graded using the Common Terminology Criteria for Adverse Events version 4.0. RESULTS: A total of 64 patients were identified for inclusion. At baseline, pain severity measured by the Brief Pain Inventory Short Form was 6.69 ± 1.42. This improved at 1 (5.85 ± 1.73), 3 (5.91 ± 1.82), and 6 months (6.05 ± 1.72; p < 0.050). Participants also reported improvements in severity as measured by the Short Form-McGill Pain Questionnaire-2 and pain visual analogue scale at the same time points (p < 0.050). Participants also reported improvements in anxiety symptoms, sleep quality, and general health-related quality of life (p < 0.050), as measured by validated measures. Five patients (7.81%) reported 50 (78.13%) adverse events. DISCUSSION: This study represents the outcomes in individuals with complex regional pain syndrome prescribed cannabis-based medicinal products. These suggest initiation of cannabis-based medicinal products is associated with improvements in patient-reported outcome measures. While these findings are consistent with the literature, they must be interpreted with caution, considering the limitations of this study. CONCLUSION: Cannabis-based medicinal products were associated with improvements in pain severity and interference. Participants also reported improvements in important metrics of health-related quality of life. This supports further research through high-quality randomized controlled trials to ascertain the efficacy of cannabis-based medicinal products in improving complex regional pain syndrome symptoms.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".