Pediatric opioid fatalities: What can we learn for prevention?
Bibliographic record
Abstract
The aim of this study was to highlight 19 cases investigated by the North Carolina Office of the Chief Medical Examiner over the last 12 years involving accidental or undetermined manner of death opioid ingestions leading to fatalities in young children. These pediatric ingestions have closely mirrored the opioid epidemic in adults transitioning from prescription medications to illicit drugs including fentanyl and fentanyl analogues. Unlike a typical adult ingestion for purposes of self-harm or pleasure, poisonings in toddlers and infants are usually the result of curiosity, exploration, a decreased sense of danger, or imitation of adult or older sibling behavior. Eleven of the decedents were between the ages of 8 and 24 months. Among the cases were 12 prescription opioid exposure deaths and 7 illicit drug poisonings. A majority of the decedents were found unresponsive in an unkept home and/or in unsafe sleeping spaces with easy access to drugs or drug materials, which stresses the importance of safe pediatric sleeping conditions. After a complete pathological investigation, several of the cases had physical or scene evidence demonstrating that foil, plastic, or paper small enough to be ingested can contain enough potent opioid to cause death. Details from the toxicological investigation are included for each case to provide postmortem whole blood drug concentrations for forensic practitioners. Accidental pediatric poisonings are preventable. Risk reduction through improving awareness and education of the dangers of opioids is a key factor in mitigating these tragedies.
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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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".