Predictors of Treatment Outcomes for Patients with Opioid Use Disorder
Bibliographic record
Abstract
Background: Opioid-related mortality rates have steeply risen over the past decade, simultaneous to the increased prevalence of more potent synthetic opioids such as fentanyl in the street drug supply. Many patients with opioid use disorder (OUD) also use cannabis, which has been suggested to reduce opioid use in this population. The purpose of this thesis is to gain a deeper understanding of treatment outcomes for patients with OUD since the onset of the fentanyl era and subsequent legalization of cannabis in Canada, and to evaluate the potential association of cannabis use and treatment outcomes. Methods: We used data from a large sample of patients receiving treatment (methadone or buprenorphine) for OUD from fifty-four clinical sites across Ontario, Canada between 2018 and 2023. We conducted three studies aimed at evaluating various aspects of treatment outcomes for patients with OUD. We specifically focused on the potential implications of cannabis use in these patients. Results: The main conclusions of this work include: 1) although patients on methadone are more likely to stay in treatment than those on buprenorphine, the treatment type did not affect continued non-prescribed opioid use in patients who completed 12-months of follow-up; 2) approximately half of the patients with OUD used cannabis which did not improve treatment outcomes; 3) cannabis use was associated with a heightened propensity for suicidal ideation, irrespective of the frequency of use. Conclusion: We identified several trends associated with response to treatment amongst patients using opioids in the current fentanyl era, and since the legalization of cannabis in Canada. The findings of this thesis are highly generalizable to the typical patient with OUD, and help to identify potentially higher-risk individuals who may benefit from more intensive treatment programs. Future studies are needed to gain a deeper understanding of treatment outcomes for patients with OUD.
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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 | 0.004 |
| 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".