Comment on: Machine Learning for Understanding and Predicting Injuries in Football
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1186/s40798-024-00745-1.pdf
Reference18 articles.
1. Majumdar A, Bakirov R, Hodges D, Scott S, Rees T. Machine learning for understanding and predicting injuries in football. Sports Med Open. 2022;8:73.
2. Bullock GS, Mylott J, Hughes T, Nicholson KF, Riley RD, Collins GS. Just how confident can we be in predicting sports injuries? A systematic review of the methodological conduct and performance of existing musculoskeletal injury prediction models in sport. Sport Med. 2022;52(10):2469–82.
3. Wolff RF, Moons KG, Riley RD, Whiting PF, Westwood M, Collins GS, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Int Med. 2019;170(1):51–8.
4. Bullock GS, Hughes T, Sergeant JC, Callaghan MJ, Riley R, Collins G. Methods matter: clinical prediction models will benefit sports medicine practice, but only if they are properly developed and validated. British J Sports Med. 2021;55(23):1319–21.
5. Bullock GS, Hughes T, Sergeant JC, Callaghan MJ, Riley RD, Collins GS. Clinical prediction models in sports medicine: a guide for clinicians and researchers. J Orthop Sport Phys Ther. 2021;51(10):517–25.
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