Pathways of care following opioid overdose among people with opioid use disorder: A multilevel cohort study
Bibliographic record
Abstract
BACKGROUND: The care that people with opioid use disorder (OUD) receive during hospitalizations for opioid overdoses present opportunities for support, yet initiation of opioid agonist treatment (OAT) remains low. Therefore, we sought to determine factors associated with treatment initiation following hospitalization for an opioid overdose. METHODS: We conducted a population-based cohort study of people with OUD discharged from hospital following an opioid overdose between January 1, 2014 and December 31, 2021 in Ontario, Canada. Our primary outcome was initiation of treatment (OAT and/or safer opioid supply) within 30 days of discharge. Proportional hazards frailty models were used to account for the clustering of hospital and geographic-level variables with cause-specific hazards ratios calculated for each factor. RESULTS: Overall, 13,253 individuals experienced 22,848 opioid overdoses and were discharged from 175 hospitals across Ontario. Treatment was initiated in 10.3 % of opioid overdoses. Person-related variables associated with treatment initiation included hepatitis C diagnoses (HR=1.15, 95 % CI=1.01-1.30) and public drug benefit eligibility (HR=1.50, 95 % CI=1.36-1.66). Longer stays in hospital were also associated with a significant increase in treatment initiation over the first 10 days of follow-up only (HR=1.10 per 5 days in hospital; 95 % CI=1.06-1.15). People discharged from regions with the highest quantile of fatal opioid overdose rates had an increased hazard of treatment initiation (HR=1.26; 95 % CI=1.06-1.51), compared to regions in the lowest quantile. CONCLUSION: The identification of factors associated with treatment initiation following overdose may be associated with promoting longer stays in hospital and enhancing accessibility in regions with less experience managing opioid overdoses.
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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 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".