Opioid deaths in children in Ontario: A province-wide study
Bibliographic record
Abstract
Objectives: Opioid-related deaths are an ongoing concern. There have been increasing numbers of fentanyl-related adult deaths with limited knowledge of the characteristics and circumstances of opioid toxicity deaths in children. Our aim was to address this using province-wide data capturing all deaths in children under the age of 10 years in Ontario. Methods: Data were extracted from the opioid investigative aid database at the Office of the Chief Coroner from the implementation of the system from October 1, 2017, to October 31, 2021. This collects all opioid-related deaths in Ontario (population 14.7 million). A chart review was undertaken on all deaths under 10 years of age. Patient characteristics were calculated as percentages; descriptive analysis was conducted. Results: Ten deaths in children under the age of 10 occurred during the study period. The average age was 1.9 years with the oldest being 4 years and 9 months. The causative opioid was fentanyl alone in four cases (40%), fentanyl and other drugs in four cases (40%), and hydromorphone and methadone in one case each (10%). Most cases involved improperly stored medication or illicit substances. All children who died had previous child protection service involvement, and at least 70% of their families had previous police involvement. Conclusions: Fentanyl was the primary substance involved in 80% of deaths. Several potential areas of system change include education on fentanyl risk to young children, careful storage of illicit substances, and implications for how the child protection system intervenes in homes where the use of opioids and illicit substance use is reported to occur.
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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 | 0.002 |
| 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 | 0.000 |
| 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".