<scp>UK</scp> medical cannabis registry: A clinical outcome analysis of medical cannabis therapy in chronic pain patients with and without co‐morbid sleep impairment
Bibliographic record
Abstract
INTRODUCTION: Chronic pain (CP) affects 35.0%-51.3% of the UK population, with 67%-88% reporting sleep disturbances. Cannabis-based medicinal products (CBMPs) have shown therapeutic potential in managing CP. Evidence suggests poor sleep worsens pain perception; therefore, this study aimed to assess patient-reported outcome measures (PROMs) following CBMP treatment in CP patients with and without co-morbid sleep impairment. METHODS: A prospective cohort study of CP patients from the UK Medical Cannabis Registry was conducted. Participants were separated by baseline single-item sleep quality scale (SQS) score into sleep impaired (SQS ≤3) and unimpaired (SQS ≥4) cohorts. The primary outcome assessed changes in PROMs from baseline to 1-, 3-, 6-, and 12-months. Participants completed the following: SQS, General Anxiety Disorder-7, EQ-5D-5L, Brief Pain Inventory (BPI), and Short-Form McGill Pain Questionnaire-2. Significance was defined as p < 0.050. RESULTS: 1139 participants met the inclusion criteria (sleep impaired: n = 517, 45.4%; sleep unimpaired: n = 622, 54.61%). The sleep impaired cohort showed improvements in all PROMs at each follow-up (p < 0.010). The sleep unimpaired cohort showed similar results (p < 0.050), except in SQS and ED-5Q-5L: self-care and anxiety/depression scores (p > 0.050). However, the sleep impaired cohort observed greater improvements in BPI pain severity (p < 0.050) and SQS (p < 0.001) than the sleep unimpaired cohort at all follow-ups. 2817 adverse events were self-reported between both cohorts (p = 0.197). DISCUSSION: These findings align with literature that shows associated improvements in pain outcomes following CBMP administration. Sleep impaired individuals were more likely to experience greater pain severity improvements. However, this was not confirmed on multivariate logistic regression analysis and instead may be confounded by baseline pain severity. CONCLUSION: Whilst these results show promise for the effects of CBMPs on CP, they must be examined within the limitations of the study design. These findings provide further evidence to support the design of subsequent randomized controlled trials to verify causality between CBMPs and pain outcomes.
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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 | 0.028 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".