Authorization Patterns, Safety, and Effectiveness of Medical Cannabis in Quebec
Bibliographic record
Abstract
Introduction: Despite increasing demand for data, little is known about the authorization patterns, safety, and effectiveness of medical cannabis products. Materials and Methods: We conducted a 2 year observational study of adult patients who were legally authorized a medical cannabis product from a single licensed producer; we captured and analyzed authorized cannabis use patterns by cannabinoid profile (tetrahydrocannabinol [THC]-dominant; cannabidiol [CBD]-dominant; and balanced (THC:CBD) and clinical outcomes using standardized outcome measures every 3 months for 12 months at a network of medical cannabis clinics in Quebec, Canada. Results: We recruited 585 patients (average age 56.5 years), of whom 61% identified as female and 85% reported pain as their primary complaint. Over 12 months, there was a significant increase in the number of products authorized ( Z =2.59, p =0.01). The proportion of authorizations for a THC-dominant or CBD-dominant product increased relative to the proportion of authorizations for a balanced (THC:CBD) product (all p <0.01). Symptom improvement over time was observed for pain, tiredness, drowsiness, anxiety, and well-being. Patients authorized THC-dominant products exhibited less symptom improvement for anxiety and well-being relative to those authorized CBD-dominant or balanced (THC:CBD) products. Medical cannabis was well tolerated across all product profiles. Conclusion: These real-world data reveal changes in medical cannabis authorization patterns and suggest that symptom improvement may vary by cannabinoid profile over 12 months of follow-up.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".