Predictors of severe outcome following opioid intoxication in children
Bibliographic record
Abstract
Introduction While the opioid crisis has claimed the lives of nearly 500,000 in the U.S. over the past two decades, and pediatric cases of opioid intoxications are increasing, only sparse data exist regarding risk factors for severe outcome in children following an opioid intoxication. We explore predictors of severe outcome (i.e., intensive care unit [ICU] admission or in-hospital death) in children who presented to the Emergency Department with an opioid intoxication.Methods In this prospective cohort study we collected data on all children (0–18 years) who presented with an opioid intoxication to the 50 medical centers in the US and two international centers affiliated with the Toxicology Investigators Consortium (ToxIC) of the American College of Medical Toxicology, from August 2017 through June 2020, and who received a bedside consultation by a medical toxicologist. We collected relevant demographic, clinical, management, disposition, and outcome data, and we conducted a multivariable logistic regression analysis to explore predictors of severe outcome. The primary outcome was a composite severe outcome endpoint, defined as ICU admission or in-hospital death. Covariates included sociodemographic, exposure and clinical characteristics.Results Of the 165 (87 females, 52.7%) children with an opioid intoxication, 89 (53.9%) were admitted to ICU or died during hospitalization, and 76 did not meet these criteria. Seventy-four (44.8%) children were exposed to opioids prescribed to family members. Fentanyl exposure (adjusted OR [aOR] = 3.6, 95% CI: 1.0–11.6; p = 0.03) and age ≥10 years (aOR = 2.5, 95% CI: 1.2–4.8; p = 0.01) were independent predictors of severe outcome.Conclusions Children with an opioid toxicity that have been exposed to fentanyl and those aged ≥10 years had 3.6 and 2.5 higher odds of ICU admission or death, respectively, than those without these characteristics. Prevention efforts should target these risk factors to mitigate poor outcomes in children with an opioid intoxication.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".