Medicinal Cannabis Use for Rheumatic Conditions in the <scp>US</scp> Versus Canada: Rationale for Use and Patient–Health Care Provider Interactions
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
OBJECTIVE: Understanding how medical cannabis (MC) use is integrated into medical practice for rheumatic disease management is essential. We characterized rationale for MC use, patient-physician interactions around MC, and MC use patterns among people with rheumatic conditions in the US and Canada. METHODS: We surveyed 3406 participants with rheumatic conditions in the US and Canada, with 1727 completing the survey (50.7% response rate). We assessed disclosure of MC use to health care providers, MC authorization by health care providers, and MC use patterns and investigated factors associated with MC disclosure to health care providers in the US versus Canada. RESULTS: Overall, 54.9% of US respondents and 78.0% of Canadians reported past or current MC use, typically because of inadequate symptom relief from other medications. Compared to those in Canada, fewer US participants obtained MC licenses, disclosed MC use to their health care providers, or asked advice on how to use MC (all P values <0.001). Overall, 47.4% of Canadian versus 28.2% of US participants rated their medical professionals as their most trusted information source. MC legality in state of residence was associated with 2.49 greater odds of disclosing MC use to health care providers (95% confidence interval: 1.49-4.16, P < 0.001) in the US, whereas there were no factors associated with MC disclosure in Canada. Our study is limited by our convenience sampling strategy and cross-sectional design. CONCLUSION: Despite widespread availability, MC is poorly integrated into rheumatic disease care, with most patients self-directing use with minimal or no clinical oversight. Concerted efforts to integrate MC into education and clinical policy is critical.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it