Screening risk factors for the occurrence of wedge effects in intramedullary nail fixation for intertrochanteric fractures in older people via machine learning and constructing a prediction model: a retrospective study
This study employed three machine learning variable selection methods-the LASSO, RFE, and Boruta algorithms-to construct a V-effect predictive model. The model enables orthopedic surgeons to better understand the risk factors associated with the V-effect and provides a reference for surgeons to implement appropriate measures to reduce the incidence of the V-effect.
Authors
- Jianbo Sun
- Ruguo Zhang
- Guojin Hou (ORCID: https://orcid.org/0000-0003-0949-4302)
- Qianming Chen (ORCID: https://orcid.org/0000-0002-5371-4432)
- Chen Liu (ORCID: https://orcid.org/0009-0008-2829-9135)
- Zhe Xu (ORCID: https://orcid.org/0000-0002-9705-8924)
- Zhi Zhou (ORCID: https://orcid.org/0000-0002-6052-2658)
- Guang Tian
Institutions
- Guiyang Medical University (CN)
- Anshun University (CN)
- Guiyang University (CN)
- First People’s Hospital of Zunyi (CN)
- The First People's Hospital of Guiyang (CN)
Publication Details
- Journal
- BMC Musculoskeletal Disorders
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1186/s12891-025-08619-7
- Citations
- 2
- Primary Topic
- Hip and Femur Fractures
- Type
- article
- Field-Weighted Citation Impact
- 4.01
Funders
- Science and Technology Program of Guizhou Province