Assessment of Clinical Outcomes in Patients With Osteoarthritis: Analysis From the UK Medical Cannabis Registry
Bibliographic record
Abstract
Osteoarthritis accounts for 0.6% of disability-adjusted life years globally. There is a paucity of research focused on cannabis-based medicinal products (CBMPs) for osteoarthritic chronic pain management. This study aims to assess changes in validated patient-reported outcome measures (PROMs) and CBMP clinical safety in patients with osteoarthritis. A prospective case series from the UK Medical Cannabis Registry was analyzed. Primary outcomes were changes in the Brief Pain Inventory (BPI), McGill Pain Questionnaire (MPQ2), EQ-5D-5L, Generalized Anxiety Disorder-7 (GAD-7) questionnaire, and Single-Item Sleep Quality Scale (SQS) at 1-, 3-, 6-, and 12-month follow-ups from baseline. Common Terminology Criteria for Adverse Events v.4.0 was used for adverse event (AE) analysis. Statistical significance was defined as p < 0.050. Seventy-seven patients met inclusion criteria. CBMP initiation correlated with BPI pain severity (p = 0.004), pain interference (p = 0.005), and MPQ2 (p = 0.017) improvements at all follow-ups compared to baseline. There were improvements in the EQ-5D-5L index (p = 0.026), SQS (p < 0.001), and GAD-7 (p = 0.038) up to 6 and 3 months, respectively. Seventeen participants (22.08%) recorded 76 mild AEs (34.86%), 104 moderate AEs (47.71%), and 38 severe AEs (17.43%). Though causality cannot be assumed in this observational study, results support development of randomized control trials for osteoarthritis pain management with CBMPs.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.001 | 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".