The impact of benzodiazepine use in patients enrolled in opioid agonist therapy in Northern and rural Ontario
Bibliographic record
Abstract
BACKGROUND: Benzodiazepine use is common among patients in opioid agonist therapy; this puts patients at an increased risk of overdose and death. In this study, we examine the impact of baseline and ongoing benzodiazepine use, and whether patients are more likely to terminate treatment with increasing proportion of benzodiazepine positive urine samples. We also study whether benzodiazepine use differs by geographic location. METHODS: We conducted a retrospective cohort study using anonymized electronic medical records from 58 clinics offering opioid agonist therapy in Ontario. One-year treatment retention was the primary outcome of interest and was measured for patients who did and did not have a benzodiazepine positive urine sample in their first month of treatment, and as a function of the proportion of benzodiazepine-positive urine samples throughout treatment. Cox proportional hazard model was used to characterize one-year retention. RESULTS: Our cohort consisted of 3850 patients, with the average retention rate of 43.4%. Baseline benzodiazepine users had a retention rate of 39.9% and non-users had a retention rate of 44%. Patients who were benzodiazepine negative on admission benefited from an increased median days retained of 265 vs. 215 days. Patients with more than 75% of urines positive for benzodiazepines were 175% more likely to drop out of treatment than those patients with little or no benzodiazepine use. CONCLUSIONS: Baseline benzodiazepine use is predictive of decreased retention. Patients who have a higher proportion of benzodiazepine-positive urine samples are more likely to drop out of treatment compared to those who have little or no benzodiazepine detection in their urine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".