2022
DOI: 10.1097/gox.0000000000004472
|View full text |Cite
|
Sign up to set email alerts
|

Machine Learning Improves Functional Upper Extremity Use Capture in Distal Radius Fracture Patients

Abstract: Summary: Current outcome measures, including strength/range of motion testing, patient-reported outcomes (PROs), and motor skill testing, may provide inadequate granularity in reflecting functional upper extremity (UE) use after distal radius fracture (DRF) repair. Accelerometry analysis also has shortcomings, namely, an inability to differentiate functional versus nonfunctional movements. The objective of this study was to evaluate the accuracy of machine learning (ML) analyses in capturing UE functional move… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

0
0
0

Year Published

2024
2024
2025
2025

Publication Types

Select...
2

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
references
References 17 publications
0
0
0
Order By: Relevance