Clinical Outcome Data of First Cohort of Chronic Pain Patients Treated With Cannabis‐Based Sublingual Oils in the United Kingdom: Analysis From the UK Medical Cannabis Registry
Bibliographic record
Abstract
Cannabis-based medicinal products (CBMPs) are an emerging therapeutic option in the management of primary chronic pain, using the role of the endocannabinoid system in modulating central and peripheral pain processes. Despite promising preclinical data, there is a paucity of high-quality evidence to support the use of CBMPs for chronic pain. This study aimed to investigate the health-related quality-of-life outcomes of patients with chronic pain who were prescribed CBMP oil preparations (Adven, Curaleaf International, Guernsey, UK). This study is a case series of patients from the UK Medical Cannabis Registry, who were treated with CBMP oils for an indication of chronic pain. The primary outcomes were the changes in Brief Pain Inventory short form, Short-Form McGill Pain Questionnaire-2, Visual Analog Scale Pain, General Anxiety Disorder-7, Sleep Quality Scale, and EQ-5D-5L, at 1, 3, and 6 months. One hundred ten patients were included. Significant improvements in Sleep Quality Scale, EQ-5D-5L pain and discomfort subscale, and Brief Pain Inventory Interference Subscale (P < .05) at 1, 3, and 6 months were demonstrated. There were no notable differences between cannabis-naïve and previous cannabis users in quality-of-life outcomes. The adverse event incidence was 30.0%, with most (n = 58; 92.1%) adverse events being mild or moderate in intensity. Treatment of chronic pain with Adven CBMP oils was associated with an improvement in pain-specific outcomes, health-related quality of life, and self-reported sleep quality. Relative safety was demonstrated over medium-term prescribed use. While these findings must be treated with caution considering the limitations of study design, they can inform future clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.029 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 teacher head, 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".