Opioid agonist therapy during residential treatment of opioid use disorder: Cohort study on access and outcomes.
Bibliographic record
Abstract
OBJECTIVE: To determine access to opioid agonist therapy (OAT) for those entering residential treatment for opioid use disorder; to report on treatment outcomes for those taking OAT and those not taking OAT; and to determine the association between OAT use and residential treatment completion. DESIGN: Retrospective cohort study. SETTING: Ontario. PARTICIPANTS: Patients with opioid use disorder admitted to publicly funded residential treatment programs in the province of Ontario between January 1, 2013, and December 31, 2016. MAIN OUTCOME MEASURES: Access to OAT during residential treatment using descriptive statistics. Treatment outcomes (ie, completed the program, voluntarily left early, involuntary discharged, and other) for the entire cohort and for the OAT and non-OAT groups using descriptive statistics. Association between OAT use at admission and treatment completion (a binary outcome) using bivariate and multivariate models. RESULTS: Among an identified cohort of 1910 patients with opioid use disorder, 52.8% entered programs that permitted access to OAT. Overall, 56.8% of patients completed treatment, 23.3% voluntarily left early (eg, were no-shows, dropped out), 17.0% were involuntarily discharged, and 2.9% were discharged early for other reasons. Those taking OAT were as likely to complete treatment as those not taking OAT (53.9% vs 57.5%, respectively; adjusted odds ratio of 1.07, 95% CI 0.77 to 1.38). CONCLUSION: This study demonstrates 2 large gaps in care for patients with opioid use disorder. First, these patients have poor access to OAT-the first-line treatment of opioid use disorder-while in publicly funded residential treatment programs; and second, many are involuntarily discharged from treatment. Additionally, this study indicates that patients taking OAT have similar likelihood of completing residential treatment as those not taking OAT do. Limitations of this study are that it is based on observational data for patients who self-selected before admission to use OAT or not, and it is likely not all confounders were accounted for.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".