Substituting Medical Cannabis for Medications Among Patients with Rheumatic Conditions in the United States and Canada
Bibliographic record
Abstract
OBJECTIVE: There are numerous reports of people substituting medical cannabis (MC) for medications. Our obejctive was to investigate the degree to which this substitution occurs among people with rheumatic conditions. METHODS: In a secondary analysis from a cross-sectional survey conducted with patient advocacy groups in the US and Canada, we investigated MC use and medication substitution among people with rheumatic conditions. We subgrouped by whether participants substituted MC for medications and investigated differences in perceived symptom changes and use patterns, including methods of ingestion, cannabinoid content (cannabidiol vs delta-9-tetrahydrocannabinol [THC]), and use frequency. RESULTS: Among 763 participants, 62.5% reported substituting MC products for medications, including nonsteroidal anti-inflammatory drugs (54.7%), opioids (48.6%), sleep aids (29.6%), and muscle relaxants (25.2%). Following substitution, most participants reported decreases or cessation in medication use. The primary reasons for substitution were fewer adverse effects, better symptom management, and concerns about withdrawal symptoms. Substitution was associated with THC use and significantly higher symptom improvements (including pain, sleep, anxiety, and joint stiffness) than nonsubstitution, and a higher proportion of substitutors used inhalation routes than those who did not. CONCLUSION: Although the determination of causality is limited by our cross-sectional design, these findings suggest that an appreciable number of people with rheumatic diseases substitute medications with MC for symptom management. Inhalation of MC products containing some THC was most commonly identified among those substituting, and disease characteristics did not differ by substitution status. Further study is needed to better understand the role of MC for symptom management in rheumatic conditions.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".