Medical Cannabis Use by Rheumatology Patients Following Recreational Legalization: A Prospective Observational Study of 1000 Patients in Canada
Bibliographic record
Abstract
OBJECTIVE: Recreational legalization of cannabis may influence the medical use by patients. When only medical access was legally available in Canada, 4.3% of rheumatology patients reported use. With the current recreational legalization, we have reexamined the prevalence and characteristics of medical cannabis use in this same rheumatology setting. METHODS: Consecutively attending rheumatology patients participated in an onsite survey comprising the following two questionnaires: 1) demographic and disease information completed by the rheumatologist and 2) patient anonymous questionnaire of health status, cannabis use (recreational and/or medicinal), and characteristics of cannabis use. RESULTS: Of 1047 attendees from June to August 2019, with 1000 participating, medical cannabis had been used by 12.6% of patients (95% confidence interval 10.7%-14.8%), with half continuing use for mostly pain relief. Discontinuation was due to lack of effect in 57% of patients and side effects in 28% of patients. Ever medical users were younger (61.2 vs. 64.9 years; P = 0.006), more likely unemployed/disabled (16.7% vs. 5.9%; P < 0.001), and had more previous (47.6% vs. 25.5%; P < 0.001) and current recreational cannabis use (17.5% vs. 3.1%; P < 0.001) than nonusers. Most patients used multiple methods of administration, including smoking, vaporizing, and using oral oil preparations, but were poorly knowledgeable of product content, which was bought solely via the legal medical route by only 20%, and only one-third disclosed their use to the rheumatologist. CONCLUSION: Medical cannabis use has tripled for rheumatology patients since recreational legalization, with users being younger, not working, and having recreational cannabis experience. Concerning issues are the poor knowledge of the product being used, access via the nonmedical route, and nondisclosure to the physician.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".