A Machine Learning Approach to Predicting Radiographic Outcomes of Nonsurgically Treated Distal Radius Fractures
This study developed an ML model that accurately predicts 6-week radiographic outcomes in nonsurgically treated distal radius fractures. Postreduction parameters were the strongest predictors, underscoring the importance of a high-quality closed reduction. This study validates the potential of ML as a predictive tool in this setting.
Authors
- Eric Taleghani (ORCID: https://orcid.org/0000-0002-5034-945X)
- James Rex (ORCID: https://orcid.org/0000-0001-8016-1194)
- Taylor Shackleford
- Thomas M. Talavage (ORCID: https://orcid.org/0000-0003-4977-6398)
- Lyu R
- Matthew M. Florczynski (ORCID: https://orcid.org/0000-0002-8863-3720)
- Isaac C. Hale (ORCID: https://orcid.org/0000-0002-3210-9241)
Institutions
- Polskie Towarzystwo Gastroenterologii (PL)
- Centre Alpien de Phytogéographie (CH)
- University of Cincinnati Medical Center (US)
Publication Details
- Journal
- Journal of the American Academy of Orthopaedic Surgeons
- Published
- 2026-01-05
- DOI
- https://doi.org/10.5435/jaaos-d-25-01150
- Primary Topic
- Orthopedic Surgery and Rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 0.00