Evaluating the association between urine drug screening frequency and retention in opioid agonist treatment in Ontario, Canada: a retrospective cohort study
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to evaluate how urine drug screening (UDS) frequency is associated with retention in opioid agonist treatment (OAT). METHODS: Data for this retrospective cohort study of 55 921 adults in OAT in Ontario, Canada, were derived from administrative sources between 1 January 2011 and 31 December 2015. All patient information was linked anonymously across databases using encrypted health card numbers. Descriptive statistics were calculated for comparing UDS frequency groups using standardised differences (d) where d less than 10% indicated a statistically significant difference. A logistic regression model was then used to calculate ORs adjusting for baseline covariates, including sex, age, location of residence, income quintile, mental disorders, HIV status and deep tissue infections. RESULTS: Over 70% of the cohort had four or more UDS tests per month (weekly or more UDS). Significant associations were observed between UDS frequency and 1-year treatment retention in OAT biweekly (adjusted OR (aOR)=3.20, 95% CI 2.75 to 3.75); weekly UDS (aOR=6.86, 95% CI 5.88 to 8.00) and more than weekly (aOR=8.03, 95% CI 6.87 to 9.38) using the monthly or less groups as the reference. CONCLUSION: This study identified an association between weekly UDS and 1-year treatment retention in OAT. There is an active discussion within Canada about the utility of UDS. The lack of evidence for the impact of UDS on retention has left it open to some to argue they simply provide a barrier to patient engagement. Therefore, it is timely of this study to demonstrate that more frequent urine testing is not associated with a reduction in treatment retention.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".