UK Medical Cannabis registry: an analysis of clinical outcomes of medicinal cannabis therapy for chronic pain conditions
Bibliographic record
Abstract
OBJECTIVES: To explore pain-specific, general health-related quality of life (HRQoL), and safety outcomes of chronic pain patients prescribed cannabis-based medicinal products (CBMPs). METHODS: A case series was performed using patients with chronic pain from the UK Medical Cannabis Registry. Primary outcomes were changes in Brief Pain Inventory short-form (BPI), Short-form McGill Pain Questionnaire-2 (SF-MPQ-2), Visual Analogue Scale-Pain (VAS), General Anxiety Disorder-7 (GAD-7), Sleep Quality Scale (SQS), and EQ-5D-5L, at 1, 3, and 6 months from baseline. Statistical significance was defined at p-value<0.050. RESULTS: -tetrahydrocannabinol and cannabidiol daily doses were 2.0mg (range:0.0-442.0mg) and 20.0mg (range:0.0-188.0mg) respectively. Significant improvements were observed within BPI, SF-MPQ-2, GAD-7, SQS, EQ-5D-5 L index, and VAS measures at all timepoints (p<0.050). Seventy-five adverse events (39.47%) were reported, of which 37 (19.47%) were rated as mild, 23 (12.11%) as moderate, and 14 (7.37%) as severe. Nausea (n=11; 5.8%) was the most frequent adverse event. CONCLUSION: An association was identified between patients with chronic pain prescribed CBMPs and improvements in pain-specific and general HRQoL outcomes. Most adverse events were mild to moderate in severity, indicating CBMPs were well tolerated. Inherent limitations of study design limit its overall applicability.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".