Characteristics of Opioid-Users Whose Death Was Related to Opioid-Toxicity: A Population-Based Study in
Bibliographic record
Abstract
Background: The impact of the prescription opioid public health crisis has been illustrated by the dramatic increase in opioid-related deaths in North America. We aimed to identify patterns and characteristics amongst opioid-users whose cause of death was related to opioid toxicity. Methods: This was a population-based study of Ontarians between the years 2006 and 2008. All drug-related deaths which occurred during this time frame were reviewed at the Office of the Chief Coroner of Ontario, and opioid-related deaths were identified. Medical, toxicology, pathology, and police reports were comprehensively reviewed. Narratives, semi-quantitative, and quantitative variables were extracted, tabulated, and analyzed. Results: Out of 2330 drug-related deaths in Ontario, 58 % were attributed either in whole or in part, to opioids (n = 1359). Oxycodone was involved in approximately one-third of all opioid-related deaths. At least 7 % of the entire cohort used opioids that were prescribed for friends and/or family, 19 % inappropriately self-administered opioids (injection, inhalation, chewed patch), 3 % were recently released from jail, and 5 % had been switched from one opioid to another near the time of death. Accidental deaths were significantly associated with personal history of substance abuse, enrollment in methadone maintenance programs, cirrhosis, hepatitis, and cocaine use. Suicides were significantly associated with mental illness, previous suicide attempts, chronic pain, and a history of cancer.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".