The association between health conditions and cannabis use in patients with opioid use disorder receiving methadone maintenance treatment
Bibliographic record
Abstract
BACKGROUND: Cannabis is the most commonly used substance among patients in methadone maintenance treatment (MMT) for opioid use disorder. Current treatment programmes neither screen nor manage cannabis use. The recent legalisation of cannabis in Canada incites consideration into how this may affect the current opioid crisis. AIMS: Investigate the health status of cannabis users in MMT. METHOD: Patients were recruited from addiction clinics in Ontario, Canada. Regression analyses were used to assess the association between adverse health conditions and cannabis use. Further analyses were used to assess sex differences and heaviness of cannabis use. RESULTS: We included 672 patients (49.9% cannabis users). Cannabis users were more likely to consume alcohol (odds ratio 1.46, 95% CI 1.04-2.06, P = 0.029) and have anxiety disorders (odds ratio 1.75, 95% CI 1.02-3.02, P = 0.043), but were less likely to use heroin (odds ratio 0.45, 95% CI 0.24-0.86, P = 0.016). There was no association between cannabis use and pain (odds ratio 0.98, 95% CI 0.94-1.03, P = 0.463). A significant association was seen between alcohol and cannabis use in women (odds ratio 1.79, 95% CI 1.06-3.02, P = 0.028), and anxiety disorders and cannabis use in men (odds ratio 2.59, 95% CI 1.21-5.53, P = 0.014). Heaviness of cannabis use was not associated with health outcomes. CONCLUSIONS: Our results suggest that cannabis use is common and associated with psychiatric comorbidities and substance use among patients in MMT, advocating for screening of cannabis use in this population. DECLARATION OF INTEREST: None.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.003 | 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".