Comparing Polysubstance and Single-class Toxicity Deaths in Newfoundland & Labrador, Canada
Bibliographic record
Abstract
Objective: This study examined differences in demographic and substance profile trends between single-class and polysubstance drug toxicity deaths in Newfoundland and Labrador, Canada, between 2018 and 2024. Methods: The present study employed a retrospective chart review in which information was collected from death investigation charts of individuals who died from acute drug toxicity in Newfoundland and Labrador between 2018 and 2024. Data relating to demographics and toxicology of decedents was gathered from the Office of the Chief Medical Examiner. Results: 403 individuals died from acute drug toxicity in NL between 2018 and 2024. Of these, 293 deaths (72.7%) were determined accidental. Among accidental deaths, 160 deaths (55%) were caused by polysubstance toxicity and 133 deaths (45%) were caused by single-substance toxicity. Sex differences were more pronounced among females, who died more often from polysubstance than single-class deaths. Males died from single-class toxicity more often in the last three years of the study. Cocaine was the most implicated substance across toxicity types, and stimulant-opioid was the most common class combination contributing to polysubstance deaths. Geographical analysis implicates cocaine and ethanol across regions, across toxicity type. Conclusion: Accidental drug toxicity deaths continue to rise in Newfoundland & Labrador, across polysubstance and single-class toxicity. Complex patterns among sex distributions across toxicity type warrant further research. The heavy presence of cocaine, ethanol, and zopiclone, and the relatively low presence of non-pharmaceutical opioids in toxicology was in contrast to other jurisdictions in Canada and warrants attention from policy-makers and harm reduction service providers. Regional data from this small Canadian province is crucial in tailoring interventions for people who use drugs in the province.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".