Citation

Richter C, Petushek E, Grindem H, Franklyn-Miller A, Bahr R, Krosshaug T. Sports Biomech. 2021; ePub(ePub): ePub.

Copyright

(Copyright © 2021, Edinburgh University Press)

DOI

10.1080/14763141.2021.1947358

PMID

unavailable

Abstract

Classification algorithms determine the similarity of an observation to defined classes, e.g., injured or healthy athletes, and can highlight treatment targets or assess progress of a treatment. The primary aim was to cross-validate a previously developed classification algorithm using a different sample, while a secondary aim was to examine its ability to predict future ACL injuries. The examined outcome measure was 'healthy-limb' class membership probability, which was compared between a cohort of athletes without previous or future (No Injury) previous (PACL) and future ACL injury (FACL). The No Injury group had significantly higher probabilities than the PACL (p < 0.001; medium effect) and FACL group (p ≤ 0.045; small effect). The ability to predict group membership was poor for the PACL (area under curve [AUC]; 0.61

Language: en

Keywords

injury prediction; Classification algorithms; vertical drop jump