The association between medical cannabis and prescription opioid medication use in patients with early-stage cancer: A population-based study.
Bibliographic record
Abstract
228 Background: Medical cannabis (MC) and prescription opioid medication (POM) use is common among cancer patients. There is conflicting evidence on the association of cannabis with POM as to whether cannabis can help decrease/ cease opioid use. In this population-based study, we examine the association between MC authorization and cessation or reduction in POM use among patients with early stage cancer. Methods: This is a retrospective, population-based study of patients with early stage (stage I-III) cancer diagnosed between January 1, 2014 and December 31, 2018 in the province of Alberta, Canada. Cases were identified from the Alberta Cancer Registry (ACR) and linked to the provincial pharmacy information network (PIN) and the database from the College of Physician and Surgeons of Alberta (CPSA). Patient and treatment characteristic were used to identify a comparable non-MC group with prior POM use via probabilistic modelling. Descriptive statistics were used to describe differences between patients with and without a MC authorization. Modified Poisson regression was used to compare the likelihood of opioid cessation and reduction among groups. Results: We identified 8,801 patients of whom 326 (3.7%) had a MC authorization. Patients with a MC authorization were younger, had higher stage disease, underwent radiation and/or systemic therapy and had a higher total oral morphine equivalent (OME) use at baseline (p < 0.01). Patients with a MC authorization were less likely to cease POM at 9-12 months post MC authorization (RR 0.63, 95% CI 0.57-0.70), and less likely to reduce their POM dose by 25% (RR 0.79, 95% CI 0.74-0.85) and 50%. (RR 0.73, 95% CI 0.67-0.79). Conclusions: Patients with early stage, non-metastatic cancer with a MC authorization have higher rates of baseline POM use and are less likely to cease or reduce their POM use up to 1 year after MC authorization. Further study is required to understand the harms of concomitant MC and POM use and the impact on survivorship care.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".