Predictors of Opioid-Related Death During Methadone Therapy
Bibliographic record
Abstract
We aimed to examine pharmacologic, demographic and medical comorbidity risk factors for opioid-related mortality among patients currently receiving methadone for an opioid use disorder. We conducted a population-based, nested case-control study linking healthcare and coroner's records in Ontario, Canada, from January 31, 1994 to December 31, 2010. We included social assistance recipients receiving methadone for an opioid use disorder. Within this group, cases were those who died of opioid-related causes. For each case, we identified up to 5 controls matched on calendar quarter. The primary analysis examined the association between use of psychotropic drugs (benzodiazepines, antidepressants or antipsychotics) and opioid-related mortality. Secondary analyses examined the associations between baseline characteristics, health service utilization, comorbidities and opioid-related mortality. Among 43,545 patients receiving methadone for an opioid use disorder, we identified 175 (0.4%) opioid-related deaths, along with 873 matched controls. Psychotropic drug use was associated with a two fold increased risk of opioid-related death (adjusted odds ratio (OR) 2.0; 95% confidence interval (CI) 1.2 to 3.5). Specifically, benzodiazepines (adjusted OR 1.6; 95% CI 1.1 to 2.5) and antipsychotics (adjusted OR 2.3; 95% CI 1.5 to 3.5) were independently associated with opioid-related death. Other associated factors included chronic lung disease (adjusted OR 1.7; 95% CI 1.2 to 2.6), an alcohol use disorder (adjusted OR 1.9; 95% CI 1.2 to 3.2), mood disorders (adjusted OR 1.8; 95% CI 1.0 to 3.2), and a history of heart disease (adjusted OR 5.3; 95% CI 2.0 to 14.0). Psychotropic drug use is associated with opioid-related death in patients receiving methadone. Mindfulness of these factors may reduce the risk of death among methadone recipients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".