Opioid use in medical cannabis authorization adult patients from 2013 to 2018: Alberta, Canada
Bibliographic record
Abstract
Abstract Background The opioid overdose epidemic in Canada and the United States has become a public health crisis - with exponential increases in opioid-related morbidity and mortality. Recently, there has been an increasing body of evidence focusing on the opioid-sparing effects of medical cannabis use (reduction of opioid use and reliance), and medical cannabis as a potential alternative treatment for chronic pain. The objective of this study is to assess the effect of medical cannabis authorization on opioid use (oral morphine equivalent; OME) between 2013 and 2018 in Alberta, Canada. Methods All adult patients defined as chronic opioid users who were authorized medical cannabis by their health care provider in Alberta, Canada from 2013 to 2018 were propensity score matched to non-authorized chronic opioid using controls. A total of 5373 medical cannabis patients were matched to controls, who were all chronic opioid users. The change in the weekly average OME of opioid drugs for medical cannabis patients relative to controls was measured. Interrupted time series (ITS) analyses was used to assess the trend change in OME during the 26 weeks (6 months) before and 52 weeks (1 year) after the authorization of medical cannabis among adult chronic opioid users. Results Average age was 52 years and 54% were female. Patients on low dose opioids ( 100), showed a significant decrease over 6 months (− 435.5, 95% CI: − 596.8 to − 274.2) compared to controls. Conclusions This short-term study found that medical cannabis authorization showed intermediate effects on opioid use, which was dependent on initial opioid use. Greater observations of changes in OME appear to be in those patients who were on a high dosage of opioids (OME > 100); however, continued surveillance of patients utilizing both opioids and medical cannabis is warranted by clinicians to understand the long-term potential benefits and any harms of ongoing use.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".