Large Variation in Provincial Guidelines for Urine Drug Screening During Opioid Agonist Treatment in Canada
Bibliographic record
Abstract
ABSTRACT Urine drug screening (UDS) is commonly used to detect or validate self-reported substance use, particularly when beginning and maintaining opioid agonist therapy. However, there is currently no summary of the published clinical practice guidelines for UDS in Canada, and no measure of the consistency with which different provinces suggest administering UDS. Therefore, we conducted a policy scan of UDS guidelines, examining the published clinical practice guidelines for each Canadian province and extracting all relevant data in March 2017. Our Canadian guideline and policy scan found that UDS frequency recommendations vary greatly among Provinces for persons receiving opioid agonist therapy for opioid use disorder. Le dépistage des drogues par l’urine (UDS) est couramment utilisé pour détecter ou valider l’utilisation de substances auto-déclarées, en particulier lorsque l’on commence et que l’on maintient un traitement par des agonistes opioïdes (OAT). Cependant, il n’y a actuellement aucun résumé des lignes directrices de pratique clinique publiées pour le UDS au Canada, et aucune mesure de l’uniformisation avec laquelle les différentes provinces suggèrent d’administrer le UDS. Par conséquent, nous avons effectué une analyse des lignes directrices UDS, en examinant les lignes directrices de pratique clinique publiées pour chaque province canadienne et en extrayant toutes les données pertinentes en mars 2017. Notre analyse des lignes directrices et des politiques canadiennes révèle que les recommandations de fréquence UDS varient grandement d’une province à l’autre pour les personnes recevant une OAT pour trouble d’utilisation des opioïdes.
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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 | 0.017 | 0.074 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.010 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".