Population-Based Stimulant Toxicity Death Rate in Newfoundland and Labrador: A Retrospective Cohort Study
Bibliographic record
Abstract
Objective: Substance use is a growing concern in Canada that is characterized by a multitude of contributing factors. Subsequently, there has been a rise in both the harms associated with substance use as well as substance-related acute toxicity deaths. This study will quantify stimulant toxicity deaths in Newfoundland and Labrador. Methods: This study used a retrospective cohort design to characterize the sample of patients who died via stimulant toxicity in NL from January 1st, 2020 to December 31st, 2023. Results: Stimulant-related deaths in Newfoundland and Labrador increased between 2020 (n=10) and 2023 (n=31); this increase is generally in line with national trends. Males consistently surpassed females for all stimulant-related drug toxicity deaths throughout our period of observation by large ratios. Both sexes have seen upward trends in total stimulant-related drug toxicity deaths for each year of observation. Stimulants were frequently used in conjunction with opioids. We were interested in the role of polysubstances within our sample and found that almost half (48.5%) of the substances involved in stimulant-related deaths contained opioids. Conclusion: Significant increases in stimulant-related mortality warrant further study of stimulant use in the country and reinforce the need to identify effective policy solutions. Almost all (96%) stimulant-related deaths reported in NL from 2020-2023 were accidental, further justifying the need for the identification of relevant risk factors and effective initiatives aimed at reducing stimulant misuse.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".