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Record W4220983981 · doi:10.1080/15563650.2022.2038188

Predictors of severe outcome following opioid intoxication in children

2022· article· en· W4220983981 on OpenAlexafffund
Neta Cohen, Mathew Mathew, Adrienne L. Davis, Jeffrey Brent, Paul M. Wax, Suzanne Schuh, Stephen B. Freedman, Blake Froberg, Evan S. Schwarz, Joshua Canning, Laura Tortora, Christopher Hoyte, Andrew Koons, Michele M. Burns, J. McFalls, Timothy J. Wiegand, Robert G. Hendrickson, Bryan Judge, Lawrence S. Quang, Michael Hodgman, James Chenoweth, D. Adam Algren, Jennifer Carey, E. Martin Caravati, Peter Akpunonu, Ann-Jeannette Geib, Steven A. Seifert, Ziad Kazzi, Rittirak Othong, Spencer Greene, Christopher P. Holstege, Marit S. Tweet, David Vearrier, Anthony F. Pizon, Sharan Campleman, Shao Li, Kim Aldy, Yaron Finkelstein

Bibliographic record

VenueClinical Toxicology · 2022
Typearticle
Languageen
FieldMedicine
TopicOpioid Use Disorder Treatment
Canadian institutionsUniversity of CalgaryHospital for Sick ChildrenUniversity of Toronto
FundersSick Kids Foundation
KeywordsMedicineOpioidEmergency medicineLogistic regressionProspective cohort studyOpioid overdoseCohort studyPediatricsCohortEmergency departmentAnesthesiologyFentanyl(+)-NaloxoneInternal medicineAnesthesiaPsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.037
GPT teacher head0.379
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

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Citations9
Published2022
Admission routes2
Has abstractyes

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