: Examine the effect of driving time on follow-up visit attendance for children hospitalized with a traumatic brain injury (TBI). We hypothesized that families who lived further from the hospital would show poorer follow-up attendance.: 368 children admitted to the hospital with TBI.: Using a retrospective chart review, we calculated driving time from patients' homes. The primary outcome was attendance at the first appointment post-discharge. We used logistic regression to examine the effect of driving time on attendance, including an analysis of the effects of injury and sociodemographic covariates on the model.: Majority of children attended their first appointment. Patients living 30-60 min from the hospital were most likely to attend, and those living 15 min away were least likely to attend. After adjusting for patient characteristics, families with driving time of 30-60 min had significantly higher odds of returning for follow-up than those with driving time <15 min, though the significance of this relationship disappeared after specific socioeconomic (SES) factors were included.: Distance plays a significant role on follow-up attendance for pediatric patients with TBI. However, neighborhood SES may be an additional factor that influences the significance of the distance effect. TBI: Traumatic brain injury; SES: socioeconomic status; ISS: Injury severity scale; AIS: Abbreviated injury scale.

Download full-text PDF

Source
http://dx.doi.org/10.1080/02699052.2019.1690679DOI Listing

Publication Analysis

Top Keywords

driving time
24
follow-up visit
8
visit attendance
8
examine driving
8
30-60 min
8
time
6
attendance
5
driving
5
impact driving
4
time pediatric
4

Similar Publications

Study Design: Retrospective cohort study.

Objective: Frailty is defined as a state of minimal "physiologic reserve." The modified 5 factor frailty index (mFI-5) is a recently proposed metric for assessing frailty and has been previously studied as a predictor of morbidity and mortality.

View Article and Find Full Text PDF

An Improved Speed Sensing Method for Drive Control.

Sensors (Basel)

January 2025

Departamento de Ingeniería Electrónica, Universidad de Sevilla, 41092 Seville, Spain.

Variable-speed electrical drive control typically relies upon a two-loop scheme, one for torque/speed and another for stator current control. In modern drive control methods, the actual mechanical speed is needed for both loops. In practical applications, the speed is often acquired by incremental rotary encoders.

View Article and Find Full Text PDF

With advancements in autonomous driving technology, the coupling of spatial paths and temporal speeds in complex scenarios becomes increasingly significant. Traditional sequential decoupling methods for trajectory planning are no longer sufficient, emphasizing the need for spatio-temporal joint trajectory planning. The Constrained Iterative LQR (CILQR), based on the Iterative LQR (ILQR) method, shows obvious potential but faces challenges in computational efficiency and scenario adaptability.

View Article and Find Full Text PDF

Existing autonomous driving systems face challenges in accurately capturing drivers' cognitive states, often resulting in decisions misaligned with drivers' intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers' electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers' spatial cognition across two dimensions: relative distance and relative orientation.

View Article and Find Full Text PDF

Study on the Influence of Rural Highway Landscape Green Vision Rate on Driving Load Based on Factor Analysis.

Sensors (Basel)

January 2025

School of Civil Engineering Architecture and the Environment, Hubei University of Technology, Wuhan 430068, China.

The green vision rate of rural highway greening landscape is a key factor affecting the driver's visual load. Based on this, this paper uses the eye tracking method to study the visual characteristics of drivers in different green vision environments on rural highways in Xianning County. Based on the HSV color space model, this paper obtains four sections of rural highway with a green vision rate of 10~20%, green vision rate of 20~30%, green vision rate of 30~40%, and green vision rate of 40~50%.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!