Polysubstance use poisoning deaths in Canada: an analysis of trends from 2014 to 2017 using mortality data
Bibliographic record
Abstract
Abstract Background Over the past decade, rates of drug poisoning deaths have increased dramatically in Canada. Current evidence suggests that the non-medical use of synthetic opioids, stimulants and patterns of polysubstance use are major factors contributing to this increase. Methods Counts of substance poisoning deaths involving alcohol, opioids, other central nervous system (CNS) depressants, cocaine, and CNS stimulants excluding cocaine, were acquired from the Canadian Vital Statistics Death Database (CVSD) for the years 2014 to 2017. We used joinpoint regression analysis and the Cochrane-Armitage trend test for proportions to examine changes over time in crude mortality rates and proportions of poisoning deaths involving more than one substance. Results Between 2014 and 2017, the rate of substance poisoning deaths in Canada almost doubled from 6.4 to 11.5 deaths per 100,000 population (Average Annual Percent Change, AAPC: 23%, p
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.016 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".