The relationship between opioid agonist therapy satisfaction and fentanyl exposure in a Canadian setting
Bibliographic record
Abstract
BACKGROUND: While patient-reported treatment dissatisfaction is considered an important factor in determining the success of substance use disorder treatment, the levels of dissatisfaction with opioid agonist therapies (OAT) and its relationship with the risk of fentanyl exposure have not been characterized in the context of the ongoing opioid overdose crisis in the US and Canada. Our primary hypothesis was that OAT dissatisfaction was associated with an increased odds of fentanyl exposure. METHODS: Our objective was to examine self-reported treatment satisfaction among OAT patients in Vancouver, Canada and the association with fentanyl exposure. Longitudinal data were derived from 804 participants on OAT enrolled in two community-recruited harmonized prospective cohort studies of people who use drugs in Vancouver between 2016 and 2018 via semi-annual interviews and urine drug screens (UDS). We employed multivariable generalized estimating equations to examine the relationship between OAT dissatisfaction and fentanyl exposure. RESULTS: Out of 804 participants (57.0% male), 222 (27.6%) reported being dissatisfied with OAT at baseline and 1070 out of 1930 observations (55.4%) had fentanyl exposure. The distribution of OAT reported in the sample was methadone (n = 692, 77.7%), buprenorphine-naloxone (n = 82, 9.2%), injectable OAT (i.e., diacetylmorphine or hydromorphone; (n = 65, 7.3%), slow-release oral morphine (n = 44, 4.9%) and other/study medication (n = 8, 1.0%). In the multivariable analysis, OAT dissatisfaction was positively associated with fentanyl exposure (AOR = 1.34; 95% CI: 1.08-1.66). CONCLUSIONS: A substantial proportion of OAT patients in our sample reported dissatisfaction with their OAT, and more than half were exposed to fentanyl. We also found that those who were dissatisfied with their OAT were more likely to be exposed to fentanyl. These findings demonstrate the importance of optimizing OAT satisfaction in the context of the ongoing opioid overdose crisis.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".