Assessment of clinical outcomes in patients with inflammatory arthritis: analysis from the UK Medical Cannabis Registry
Bibliographic record
Abstract
The aim of this study was to assess changes in validated patient-reported outcome measures after initiation of cannabis-based medicinal products (CBMPs) and the safety of CBMPs in patients with inflammatory arthritis. A prospective case series from the UK Medical Cannabis Registry was analyzed. The primary outcomes changes were in Brief Pain Inventory, McGill Pain Questionnaire, EuroQol 5-dimension 5-level (EQ-5D-5L), Generalised Anxiety Disorder-7 questionnaire, and Single-Item Sleep Quality Scale at 1, 3, 6, and 12 months of follow-up compared with baseline. Adverse events were analyzed in accordance with Common Terminology Criteria for Adverse Events, v.4.0. Statistical significance was defined as a P -value less than 0.050. Eighty-two patients met the inclusion criteria. Initiation of CBMP treatment was associated with improvements in Brief Pain Inventory, McGill Pain Questionnaire, EQ-5D-5L, Generalised Anxiety Disorder-7 questionnaire, and Single-Item Sleep Quality Scale at 1, 3, 6, and 12 months compared with baseline ( P < 0.050). There were 102 (44.35%) mild adverse events, 97 (42.17%) moderate adverse events, and 31 (13.48%) severe adverse events recorded by 21 (25.61%) participants. This study suggests that CBMP treatment is associated with pain improvement and increased health-related quality of life for inflammatory arthritis patients. While causality cannot be inferred in this observational study, the results support the development of randomized control trials for inflammatory arthritis pain management with CBMPs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| 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".