The Quebec Cannabis Registry: Investigating the Safety and Effectiveness of Medical Cannabis
Bibliographic record
Abstract
Objective: To investigate the safety and effectiveness of medical cannabis (MC) in the real-world clinical practice setting. Design: A 4-year prospective noncomparative registry of adult patients who initiated MC for a variety of indications. This paper reports on patients followed for up to 12 months, with interim visits at 3, 6, and 9 months after enrollment. Setting: Public or private outpatient clinics certified to authorize MC in the province of Quebec, Canada. Participants: Overall, 2991 adult (age ≥18 years) patients (mean age 51 years; 50.2% women) were enrolled between May 2015 and October 2018, with the last follow-up ending in May 2019. Interventions/Exposures: Cannabis products (dried, oil, or other) purchased from a Canadian licensed cannabis producer as authorized by physicians. Main Outcome Measures: The primary outcomes were self-reported pain severity, interference and relief (Brief Pain Inventory [BPI]), symptoms using the Revised Edmonton Symptom Assessment System (ESAS-r) and health-related quality of life dimensions (EQ-5D-5L) at baseline and each follow-up visit. The secondary outcomes were self-reported adverse events (AEs) and characteristics of cannabis treatment. Results: All patient-reported outcomes (BPI, ESAS-r, and EQ-5D-5L) showed a statistically significant improvement at 3 months (all p <0.01), which was maintained or further improved (for pain interference, tiredness, and well-being) over the remainder of the 12-month follow-up. Results also revealed clinically significant improvements in pain interference and tiredness, anxiety, and well-being from baseline. There were 79 AE reports (77 patients), 16 met the regulatory definition of seriousness, in which only 8 AEs were certainly or probably related to MC. Conclusions: MC directed by physicians appears to be safe and effective within 3 months of initiation for a variety of medical indications.
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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 | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".