Concordance Between Urine Drug Screening and Self-Reported Use in the Context of a Pragmatic Randomized-Controlled Trial in People with <i>Prescription-Type</i> Opioid Use Disorder: Concordance entre le dépistage de drogues dans l’urine et l’usage autodéclaré dans le contexte d’un essai pragmatique contrôlé à répartition aléatoire chez des personnes présentant un trouble lié à l'usage d’opioïdes vendus sur ordonnance
Bibliographic record
Abstract
Objective In this study, we evaluated the concordance between urine drug screening (UDS) and self-reported use in a pragmatic randomized clinical trial. Methods Our data was drawn from OPTIMA, a 24-week pragmatic multicentric open-label randomized-controlled trial comparing flexible take-home dosing of buprenorphine/naloxone to the methadone standard model of care for treating prescription-type opioid use disorder. A total of 272 participants were randomized (1:1 ratio) to methadone or buprenorphine/naloxone. Following treatment initiation, participants were followed-up every 2 weeks for 24 weeks. During each visit, participants provided urine samples for UDS and self-reported their substance use over the past 2 weeks. Self-reported use was dichotomized to align with UDS detection windows. Tetrachoric correlations and 2 × 2 contingency tables were used to estimate the sensitivity, specificity, positive predictive value and negative predictive value of self-reported use. A generalized linear mixed model assessed how substance type, time in the study, treatment assignment, study site, unstable housing, and sex impacted self-report accuracy. Results Significant differences were found between substance types ( p < 0.001) and study sites ( p < 0.001). Fentanyl, cannabis, and amphetamines consistently showed the greatest concordance between measurement methods. Hydromorphone, oxycodone, heroin, and benzodiazepines had low sensitivity and low positive predictive value. Participants from Québec showed higher concordance between UDS and self-reported use compared to those from British Columbia, Alberta, and Ontario. There was no moderating effect of treatment assignment ( p = 0.174), time in the study ( p = 0.221), unstable housing ( p = 0.733), or sex ( p = 0.321) on the concordance between UDS and self-reported use. Conclusions Our results indicate that concordance between UDS and self-reported use is impacted by several factors. Combining UDS and self-reported use could help provide a more accurate assessment of substance use. Clinical trial registration This study was registered in ClinicalTrials.gov (NCT03033732).
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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 | 0.050 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".