Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Constructing characteristic signals of head acceleration events in stock car racing.

Traffic injury prevention·2026

Same author

Longitudinal changes in radiomic features of skeletal muscle on CT images with changes in muscle strength and physical performance in older adults.

The journals of gerontology. Series A, Biological sciences and medical sciences·2026

Same author

A single site, multi-operator precision study for second-generation HR-pQCT.

JBMR plus·2026

Same author

Validation and application of automated CT analysis to musculoskeletal profiling in MVC occupants.

Traffic injury prevention·2026

Same author

Head acceleration event frequency among drivers across track types in Stock Car Auto Racing.

Traffic injury prevention·2026

Same author

Reducing Video Verification Burden: Machine Learning Classification of Head Acceleration Events in Youth Football.

Research square·2026

Search research articles

Related Experiment Video

Updated: Apr 11, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Evaluation of developmental metrics for utilization in a pediatric advanced automatic crash notification algorithm.

Andrea N Doud^1,2, Ashley A Weaver³, Jennifer W Talton⁴

¹a Wake Forest School of Medicine , Department of General Surgery , Winston-Salem , North Carolina.

Traffic Injury Prevention

|June 5, 2015

Summary

Age is the best developmental metric for pediatric Advanced Automatic Crash Notification (AACN) algorithms, predicting injury risk in motor vehicle crashes (MVCs). This research quantifies injury patterns by age to improve trauma center triage for children.

Keywords:

AACN childhood development pediatric trauma

More Related Videos

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Related Experiment Videos

Last Updated: Apr 11, 2026

Design and Analysis for Fall Detection System Simplification

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Tactile Vibrating Toolkit and Driving Simulation Platform for Driving-Related Research

Published on: December 18, 2020

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Area of Science:

Pediatric trauma research
Injury biomechanics
Public health and safety

Background:

Motor vehicle crashes (MVCs) are a leading cause of injury in children.
Timely triage to designated trauma centers (TCs) significantly improves outcomes for injured children.
Advanced Automatic Crash Notification (AACN) systems show potential for optimizing prehospital triage, but pediatric-specific algorithms are lacking.

Purpose of the Study:

To determine the optimal developmental metric (age, height, or weight) for a pediatric AACN injury risk algorithm.
To quantify the relationship between the chosen developmental metric and specific injury types in children involved in MVCs.

Main Methods:

Retrospective analysis of 11,541 child occupants (<19 years) from the NASS-CDS dataset (2000-2011).
Logistic regression models compared the predictive power of age, height, and weight for 18 injury types.
Age was selected as the covariate, categorized into age bins (0-4, 5-9, 10-14, 15-18 years).
Adjusted odds of specific injuries were calculated, controlling for gender, delta V, and restraint use.

Main Results:

Age demonstrated the highest predictive power for injury patterns compared to height and weight.
Age was a significant predictor for all 18 evaluated injury types, even after controlling for confounders.
Younger children (<5 years) had higher odds of head and spinal injuries.
Older children (15-18 years) had higher odds of thoracic, abdominal, and extremity injuries.

Conclusions:

Age is the most suitable developmental metric for pediatric AACN algorithms.
Quantifiable injury risks associated with different age groups provide a basis for improved predictive capabilities.
This age-based risk quantification is crucial for developing effective pediatric-specific AACN systems to enhance trauma care for injured children.