Opioid Initiation in Older Patients with Chronic Pain Who Received Authorized Cannabis Prescription
Bibliographic record
Abstract
Cannabis is being increasingly used to treat chronic pain, with studies suggesting that concurrent use with opioids may reduce the need for opioid medications. However, specific to opioid-naïve patients, the effects of cannabis exposure on opioid initiation are not well known. To assess the association between the authorized prescription of cannabis and opioid initiation in older patients with chronic pain. We conducted a cohort study of Ontario patients aged 66 years and older with chronic pain, without a dispensation of opioids in the past year. Patients who received an authorized prescription of cannabis between 2014 and 2019 were compared with population-based controls. Clinical data combined with Ontario medical administrative data were used to conduct the analyses. The study outcome was defined as opioid dispensations covering ≥90 days in the year following the authorized prescription of cannabis. Inverse probability of treatment weighting was used to minimize confounding, and weighted Poisson regressions were used to calculate risk ratios. In total, 3427 exposed patients and 12006 controls were included. The rates of the outcome (i.e., initiating opioid covering ≥90 days) were 1.84 and 1.19/100 person-years, respectively, in the exposed and the controls. The adjusted risk ratio (RR) was 1.54 (95% CI: 1.07–2.23). The risk was significant in male patients (RR: 1.82 (1.09–3.04)) and non-significant in females (RR: 1.35 (0.84–2.17). Results suggest that opioid-naïve older patients with chronic pain, initiating medical cannabis, were at higher risk of opioid initiation in the year following cannabis prescription.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".