Cannabis Significantly Reduces the Use of Prescription Opioids and Improves Quality of Life in Authorized Patients: Results of a Large Prospective Study
Bibliographic record
Abstract
OBJECTIVES: This article presents findings from a large prospective examination of Canadian medical cannabis patients, with a focus on the impacts of cannabis on prescription opioid use and quality of life over a 6-month period. METHODS: The Tilray Observational Patient Study took place at 21 medical clinics throughout Canada. This analysis includes 1,145 patients who had at least one postbaseline visit, with follow-up at 1, 3, and 6 months. Instruments included a comprehensive cannabis use inventory, the World Health Organization Quality of Life Short Form (WHOQOL-BREF), and a detailed prescription drug questionnaire. RESULTS: Participants were 57.6% female, with a median age of 52 years. Baseline opioid use was reported by 28% of participants, dropping to 11% at 6 months. Daily opioid use went from 152 mg morphine milligram equivalent (MME) at baseline to 32.2 mg MME at 6 months, a 78% reduction in mean opioid dosage. Similar reductions were also seen in the other four primary prescription drug classes identified by participants, and statistically significant improvements were reported in all four domains of the WHOQOL-BREF. CONCLUSIONS: This study provides an individual-level perspective of cannabis substitution for opioids and other prescription drugs, as well as associated improvement in quality of life over 6 months. The high rate of cannabis use for chronic pain and the subsequent reductions in opioid use suggest that cannabis may play a harm reduction role in the opioid overdose crisis, potentially improving the quality of life of patients and overall public health.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".