The Impact of Longitudinal Substance Use Patterns on the Risk of Opioid Agonist Therapy Discontinuation: A Repeated Measures Latent Class Analysis
Bibliographic record
Abstract
Polysubstance use is prevalent among individuals on opioid agonist treatment (OAT), yet past studies have focused primarily on distinct substances and their association with OAT retention. Data was collected from two prospective cohorts between 2005 and 2020 in Vancouver, Canada. Among 13,596 visits contributed by 1445 participants receiving OAT, we employed repeated measures latent class analysis using seven indicators and identified four longitudinal substance use classes. Using marginal structural Cox modeling, we found that compared to the primarily crack use class, the two opioid and stimulant use classes carried a higher risk of OAT discontinuation, while the primarily cannabis and crack use class had a lower OAT discontinuation risk. Our findings highlight the need for integrated treatment strategies to manage the co-use of opioids and stimulants during receipt of OAT and suggest future research should explore the potential of cannabis as a harm reduction strategy or adjunctive treatment to OAT. Word count: 150/150. Supplementary Information: The online version contains supplementary material available at 10.1007/s11469-023-01098-8.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".