Prevalence and blood concentrations of benzodiazepines and opioids in opioid‐positive death investigations in Ontario, Canada, from 2017 to 2021
Bibliographic record
Abstract
The aim of this study was to investigate the incidence of benzodiazepines in opioid-positive death investigations, including trends in frequency and combination of drugs, as well as demographic data and blood concentrations, where available. Additionally, naloxone concentrations in polysubstance compared to opioid-only cases were analyzed. This was a retrospective study that consisted of all post-mortem toxicology cases in Ontario, Canada, from January 01, 2017, to December 31, 2021, with an opioid finding in any analyzed autopsy specimen. There were 11,033 death investigations identified. The overall rate of benzodiazepine co-involvement was 54.5%. Males accounted for the majority of cases (71%), and the most affected age group was 30- to 39-year-olds. The most frequently detected opioid was fentanyl and the most frequently detected benzodiazepine was etizolam, which was also the most frequently observed opioid/benzodiazepine combination. Findings related to differences in concentrations of opioids when naloxone was also present were mostly non-significant, except for methadone. The rate of benzodiazepine detection with opioids grew faster than opioid detections overall, potentially due to the increasingly toxic drug supply. Detection of novel psychoactive drugs fluctuated more unpredictably than opioids and benzodiazepines associated with clinical use. These findings can help inform policy decisions by public health agencies in exploring harm reduction efforts, for example, education and drug-checking services.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".