Position Statement: A Pragmatic Approach for Medical Cannabis and Patients with Rheumatic Diseases
Bibliographic record
Abstract
OBJECTIVE: Pain is one reason some rheumatology patients may consider use of medical cannabis, a product increasingly perceived as a safe and neglected natural treatment option for many conditions. Legalization of recreational cannabis in Canada will promote access to cannabis. Physicians must therefore provide patients with the best evidence-based information regarding the medicinal effects and harm of cannabis. METHODS: The Canadian Rheumatology Association (CRA) mandated the development of a position statement for medical cannabis and the rheumatology patient. The current literature regarding the effects of medical cannabis for rheumatology patients was assessed, and a pragmatic position statement to facilitate patient care was developed by the Therapeutics Committee of the CRA and approved by the CRA board. RESULTS: There are no clinical trials of medical cannabis in rheumatology patients. Evidence is insufficient about the benefit of pharmaceutical cannabinoids in fibromyalgia, osteoarthritis, rheumatoid arthritis, and back pain, but there is evidence of a high risk of harm. Extrapolating from other conditions, medical cannabis may provide some symptom relief for some patients. Short-term risks of psychomotor effects can be anticipated, but longterm risks have not been determined and are of concern. CONCLUSION: Despite lack of evidence for use of medical cannabis in rheumatology patients, we acknowledge the need to provide empathetic and pragmatic guidance for patient care. This position statement aims to facilitate the dialogue between patients and healthcare professionals in a mutually respectful manner to ensure harm reduction for patients and society.
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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 | 0.018 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".