The Role of Cannabis Use in Suicidal Ideation Among Patients With Opioid Use Disorder
Bibliographic record
Abstract
OBJECTIVES: Cannabis use is associated with suicide risk in the general population; however, it is unknown if this association is also present in patients with opioid use disorder (OUD). The purpose of this study is to investigate the association between cannabis use and suicidal ideation in patients with OUD. METHODS: We conducted a multivariable logistic regression analysis to assess the association between cannabis use and suicidal ideation, amongst a large cohort of patients with OUD. Current cannabis use and suicidal ideation over the past 30 days were obtained by self-report. RESULTS: Cross-sectional data from 2335 participants with OUD were included in the analysis, of whom 51% report current cannabis use. We found a positive association between cannabis use and suicidal ideation (OR = 1.41, 95% CI 1.11, 1.80, P = 0.005). We found that men (OR = 1.84, 95% CI 1.44, 2.35, P < 0.001), younger individuals (OR = 1.02, 95% CI 1.01, 1.03), P = 0.004), and that those with more symptoms of anxiety or depression (OR = 1.16, 95% CI 1.15, 1.18, P < 0.001) were more likely to report suicidal ideation. CONCLUSIONS: Cannabis use is associated with a heightened propensity for suicidal ideation amongst patients with OUD, who are already a high-risk population. Further research into the potential harms of cannabis use in this population is required given the prevalence of its use and potential benefits in mitigating opioid withdrawal.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".