Predictive accuracy of machine learning models for conservative treatment failure in thoracolumbar burst fractures.

Journal: BMC musculoskeletal disorders

PMID: 39558324

Abstract

BACKGROUND: The management of patients with thoracolumbar burst fractures remains a topic of debate, with conservative treatment being successful in most cases but not all. This study aimed to assess the utility of machine learning models (MLMs) in predicting the need for surgery in patients with these fractures who do not respond to conservative management.

Authors

Ehsan Alimohammadi

Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza Hospital, Kermanshah, Iran. Hafez125@gmail.com.
Fatemeh Arjmandnia

Kermanshah University of Medical Sciences, Kermanshah, Iran.
Mohammadali Ataee

Department of Radiology, Kermanshah University of Medical Sciences, Kermanshah, Iran.
Seyed Reza Bagheri

Department of Neurosurgery, Kermanshah University of Medical Sciences, Imam Reza Hospital, Kermanshah, Iran.

Keywords

Adult Aged Conservative Treatment Female Humans Lumbar Vertebrae Machine Learning Male Middle Aged Predictive Value of Tests Retrospective Studies Risk Factors Spinal Fractures Thoracic Vertebrae Treatment Failure Young Adult

External Resources

View on PubMed Access via DOI PubMed (39558324)

Predictive accuracy of machine learning models for conservative treatment failure in thoracolumbar burst fractures.

Abstract

Authors

Keywords

External Resources

Popular Topics

Recent Journals