Patterns of intentional overdoses in young patients: a retrospective cohort study in Québec, Canada (2020-2022)
Bibliographic record
Abstract
Introduction: In Canada, suicide/self-harm is the second-leading cause of injury-related deaths. Data from Canadian poison centres suggest that the number of suspected intentional self-harm involving poisoning is increasing, with youth being particularly affected. Understanding this trend is crucial for developing effective interventions. Methods: We conducted a retrospective cohort study using data from the Québec poison centre's ToxiQc database. All self-harm cases in patients aged 6 to 12 and 13 to 19 years between January 1, 2020 and December 31, 2022 were included. We extracted variables on patient demographics, substance exposure, route of administration, caller location and final outcome. We used descriptive statistics to analyze the cases. Results: A total of 5241 cases were included, with 84.7% being female and a mean age of 15.9 years. Most exposures occurred at home, with school being the second most common location for the 6 to 12 years. Single-substance exposures (64%) and oral exposure (99%) were predominant. Acetaminophen (31%) (95% CI [29, 32]), antidepressants (29%) (95% CI [28, 31]) and NSAIDs (17%) (95% CI [16, 18]) were the top three categories of substances involved. Natural health products ranked among the top 10 substances, with melatonin present in 87% (27/31) of cases for ages 6 to 12 and 90% (193/214) for ages 13 to 19. A small proportion of the cases had a known unfavourable outcome with moderate effect (5.3%%) (95% CI [4.6, 6.01]) and major effect (0.9%) (95% CI [0.69, 1.3]). Conclusion: Accessibility likely explains the substances used in self-harm, with both over-the-counter and prescribed medications frequently involved. It is crucial to promote prevention messages to physicians, pharmacists and parents on safely storing a minimum amount of medication at home and assisting children in managing their medication.
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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 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".