Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score
Abstract Background Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these settings, though its predictive accuracy re...
Saved in:
Main Authors: | Mike Nsubuga, Timothy Mwanje Kintu, Helen Please, Kelsey Stewart, Sergio M. Navarro |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2025-01-01
|
Series: | BMC Emergency Medicine |
Subjects: | |
Online Access: | https://doi.org/10.1186/s12873-025-01175-2 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Impact of nonphysician, technology-guided alert level selection on rates of appropriate trauma triage in the United States: a before and after study
by: Megan E. Harrigan, et al.
Published: (2023-09-01) -
An Evaluation on the Potential of Large Language Models for Use in Trauma Triage
by: Kelvin Le, et al.
Published: (2024-10-01) -
The Suitability of the CdC field Triage for Korean Trauma Care
by: Kang Kook Choi, et al.
Published: (2020-03-01) -
Effect of triage training on nurses with Emergency severity index and Australian triage scale: Α quasi-experimental study
by: George Pontisidis, et al.
Published: (2024-10-01) -
An Effective Triage Education Method for Triage Nurses: An Overview and Update
by: Zagalioti SC, et al.
Published: (2025-02-01)