Accidental substance-related acute toxicity deaths in older adults in 2016 and 2017: a national chart review study
Bibliographic record
Abstract
INTRODUCTION: Limited research exists on substance-related acute toxicity deaths (ATDs) in older adults (≥60 years) in Canada. This study aims to examine and describe the sociodemographic characteristics, health histories and circumstances of death for accidental ATDs among older adults. METHODS: Following a retrospective descriptive analysis of all coroner and medical examiner files on accidental substance-related ATDs in older adults in Canada from 2016 to 2017, proportions and mortality rates for coroner and medical examiner data were compared with general population data on older adults from the 2016 Census. Chisquare tests were conducted for categorical variables where possible. RESULTS: From 2016 to 2017, there were 705 documented accidental ATDs in older adults. Multiple substances contributed to 61% of these deaths. Fentanyl, cocaine and ethanol (alcohol) were the most common substances contributing to death. Heart disease (33%), chronic pain (27%) and depression (26%) were commonly documented. Approximately 84% of older adults had contact with health care services in the year preceding their death. Only 14% were confirmed as having their deaths witnessed. CONCLUSIONS: Findings provide insight into the demographic, contextual and medical history factors that may influence substance-related ATDs in older adults and suggest key areas for prevention.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".