Chronic pain and accidental acute toxicity deaths in Canada, 2016–2017
Bibliographic record
Abstract
INTRODUCTION: Multiple Canadian jurisdictions have reported a pattern of chronic pain among people who died from substance-related acute toxicity. This study examined the prevalence and characteristics of those with chronic pain using data from a national study of people who died of accidental acute toxicity. METHODS: A cross-sectional analysis of accidental substance-related acute toxicity deaths that occurred in Canada between 1 January 2016 and 31 December 2017 was conducted. The prevalence of pain and pain-related conditions were summarized as counts and percentages of the overall sample. Subgroups of people with and without a documented history of chronic pain were compared across sociodemographic characteristics, health history, contextual factors and substances involved. RESULTS: From the overall sample (n = 7902), 1056 (13%) people had a history of chronic pain while 6366 (81%) had no documented history. Those with chronic pain tended to be older (40 years and older), unemployed, retired and/or receiving disability supports around the time of death. History of mental health conditions, trauma and surgery or injury was significantly more prevalent among people with chronic pain. Of the substances that most frequently contributed to death, opioids typically prescribed for pain (hydromorphone and oxycodone) were detected in toxicology more often among those with chronic pain than those without. CONCLUSION: Findings underscore the cross-cutting role of multiple comorbidities and unmanaged pain, which could compound the risk of acute toxicity death. Continued prioritization of harm reduction and regular patient engagement to assess ongoing needs are among the various opportunities for intervention.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".