8 Characterizing Opioid Overdose Deaths in Children (COODC Study)
Bibliographic record
Abstract
Abstract Background With the opioid epidemic the numbers of fentanyl-related adult deaths are increasing in Canada but there is limited knowledge of fentanyl’s effect on paediatric deaths. Objectives Our aim is to describe cases of fatal paediatric opioid toxicity, and their characteristics, to help guide public policy and health care system response. Design/Methods Patient information was collected from the opioid investigative aid (OIA) database, which collects all suspected opiate-related deaths in Ontario. A chart review was performed on patients less than 10 years of age between October 1st, 2017, to October 31st, 2021. Patient characteristics were calculated as percentages and a descriptive analysis was conducted. Results 10 childhood deaths occurred, and the average age was 1.9 (Table 1). The causative opioid was fentanyl alone in 40% of cases, fentanyl in combination in 40%, and hydromorphone and methadone with 10% each. Most cases involved improperly stored medication or illicit substances (Table )2. All had previous child protection service involvement. Conclusion In patients less than 10 years of age, there were ten opioid-related deaths over four years, all in children younger than 5 years. Fentanyl was the primary drug involved in 80% of cases. This demonstrates the rise of fentanyl-related opioid deaths in children and elucidates areas for change, including education, proper storage of illicit substances, and implications for the child protection system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| 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) | 0.002 | 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".