Comparative effectiveness of urine drug screening strategies alongside opioid agonist treatment in British Columbia, Canada: a population-based observational study protocol
Bibliographic record
Abstract
INTRODUCTION: Urine drug tests (UDTs) are commonly used for monitoring opioid agonist treatment (OAT) responses, supporting the clinical decision for take-home doses and monitoring potential diversion. However, there is limited evidence supporting the utility of mandatory UDTs-particularly the impact of UDT frequency on OAT retention. Real-world evidence can inform patient-centred approaches to OAT and improve current strategies to address the ongoing opioid public health emergency. Our objective is to determine the safety and comparative effectiveness of alternative UDT monitoring strategies as observed in clinical practice among OAT clients in British Columbia, Canada from 2010 to 2020. METHODS AND ANALYSIS: We propose a population-level retrospective cohort study of all individuals 18 years of age or older who initiated OAT from 1 January 2010 to 17 March 2020. The study will draw on eight linked health administrative databases from British Columbia. Our primary outcomes include OAT discontinuation and all-cause mortality. To determine the effectiveness of the intervention, we will emulate a 'per-protocol' target trial using a clone censoring approach to compare fixed and dynamic UDT monitoring strategies. A range of sensitivity analyses will be executed to determine the robustness of our results. ETHICS AND DISSEMINATION: The protocol, cohort creation and analysis plan have been classified and approved as a quality improvement initiative by Providence Health Care Research Ethics Board and the Simon Fraser University Office of Research Ethics. Results will be disseminated to local advocacy groups and decision-makers, national and international clinical guideline developers, presented at international conferences and published in peer-reviewed journals electronically and in print.
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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 | 0.030 | 0.039 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.006 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".