Initiation of opioid agonist therapy after hospital visits for opioid poisonings in Ontario
Bibliographic record
Abstract
Background: Emergency department visits and hospital admissions for opioid toxicity are opportunities to initiate opioid agonist therapy (OAT), which reduces morbidity and mortality in patients with opioid use disorder (OUD). The study objectives were to evaluate OAT initiation rates after a hospital encounter for opioid toxicity in Ontario, Canada, and determine whether publication of a 2018 Canadian OUD management guideline was associated with increased initiation. Methods: We conducted a retrospective, population-based serial cross-sectional study of hospital encounters for opioid toxicity among patients with OUD between Jan. 1, 2013, and Mar. 31, 2020, in Ontario, Canada. The primary outcome was OAT initiation (methadone, buprenorphine–naloxone, or slow-release oral morphine) within 7 days of discharge, measured quarterly. We examined the impact of the release of the OUD management guideline on OAT initiation rates using Autoregressive Integrated Moving Average models. Results: Among 20 702 hospital visits for opioid toxicity among patients with OUD, the median age was 35 years, and 65.1% were male. Over the study period, the percentage of visits leading to OAT initiation within 7 days rose from 1.7% or less (Q1 2013) to 5.6% (Q1 2020); however, the publication of the Canadian OUD management guideline was not associated with a significant increase in these rates (0.14% slope change, 95% confidence interval −0.11% to 0.38%; p = 0.3). Interpretation: Among hospital encounters for opioid toxicity, despite rising prevalence over time, only 1 in 18 patients were dispensed OAT within a week of discharge in early 2020. These findings highlight missed opportunities to initiate therapies proven to reduce mortality in patients with OUD.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".