Prescribing practices in opioid agonist treatment and changes in compliance to clinical dosing guidelines in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND AND AIM: In British Columbia, Canada, clinical guidelines for the treatment of opioid use disorders (OUD) were updated in 2017, during a period in which the potency and composition of the illicit drug supply changed rapidly. We aimed to describe changes in opioid agonist treatment (OAT) prescribing practices at the population level in a setting in which fentanyl and its analogs have become the primary illicit opioid of use. DESIGN, SETTING AND PARTICIPANTS: This was a population-based retrospective cohort study using three linked health administrative databases in British Columbia (BC), Canada. All individuals with at least one OAT dispensation in BC between 1 January 2014 and 31 August 2021 took part. MEASUREMENTS: To assess changes in OAT prescribing practices over time, we calculated initiation doses, dose titration intervals, maintenance doses and take-home dosing intervals stratified by medication [methadone, buprenorphine-naloxone and slow-release oral morphine (SROM)] according to recommended guidelines. FINDINGS: A total of 265 410 OAT episodes (57.5% on methadone, 34.5% on buprenorphine-naloxone and 8.0% on SROM) were initiated during the study period. Compared with the guideline recommendation, observed initiation doses were higher among all medications from 2014 (2017 for SROM) to 2021 (buprenorphine-naloxone: 14-29%; methadone: 53-66%; SROM: 26-55%). Titration intervals were shorter for all medications, consistent with guidelines for buprenorphine-naloxone (26-49%), but shorter than recommended for methadone or SROM (28-51% and 12-41%, respectively). Higher maintenance dosing was observed for methadone (68-78%) and SROM (3-21%). Take-home allowances extending beyond the recommended guideline length increased across medications (buprenorphine-naloxone: 18-35%; methadone: 50-64%; SROM: 34-39%). Changes in prescribing patterns were similar for first-time OAT initiators. CONCLUSION: In British Columbia, Canada, from 2014 to 2021, prescribers of opioid agonist treatment (OAT) appeared to initiate both new and experienced OAT clients at higher doses than guideline recommendations, titrate them more rapidly and maintain clients at higher doses. Take-home dose allowances also gradually increased.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".