Opioid-Related Deaths in Eastern Ontario from 2011 to 2016
Bibliographic record
Abstract
There has been a growing opioid crisis in the United States and Canada. The aim of this study was to analyze trends in opioid-related deaths from the Eastern Ontario Regional Forensic Pathology Unit so that prevention strategies for these deaths can be developed. The analyses included examining the opioids involved and demographic characteristics of the individuals in these deaths so that possible risk factors for opioid-related deaths could be identified. A retrospective cross-sectional analysis of the full autopsy and toxicology data between 2011 and 2016 was conducted. Trends regarding the opioids involved in the death, all opioids reported in the toxicology reports and certain nonopioid drugs reported in the toxicology reports were examined. The distribution of opioid-related death by age-group and manner of death was also conducted. Two hundred seventy-four opioid-related deaths met the inclusion criteria and were examined. The majority of individuals overdosing were male. The most frequent age range for opioid-related deaths was 45 to 54 years with increasing deaths among individuals aged 55 years and older over the period studied. Fentanyl was responsible for most deaths overall when single or multiple opioids were involved. However, hydromorphone involvement was the only opioid to have a statistically significant increase over the time period. Analysis of nonopioid-related drugs revealed extensive use of antidepressants, benzodiazepines, and their metabolites. Accident was the most common manner of death throughout all age groups except for those aged 65 years or older, where suicide was most common.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".