Skip to main content
SpringerLink
Log in

Response to Comment on: “Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care”

  • Letter to the Editor
  • Published:
Sports Medicine Aims and scope Submit manuscript

A Letter to the Editor to this article was published on 14 October 2022

The Original Article was published on 14 October 2022

The Original Article was published on 17 February 2022

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

References

  1. Berndsen J, McHugh D. Comment on: “Black box prediction methods in sports medicine deserve a red card for reckless practice: a change of tactics is needed to advance athlete care”. Sports Med. 2022. https://doi.org/10.1007/s40279-022-01770-4.

    Article  Google Scholar 

  2. Bullock GS, Hughes T, Arundale AH, Ward P, Collins GS, Kluzek S. Black box prediction methods in sports medicine deserve a red card for reckless practice: a change of tactics is needed to advance athlete care. Sports Med. 2022;52(8):1729–35.

    Article  Google Scholar 

  3. Hernán MA, Hsu J, Healy B. A second chance to get causal inference right: a classification of data science tasks. Chance. 2019;32(1):42–9.

    Article  Google Scholar 

  4. Lü L, Medo M, Yeung CH, Zhang Y-C, Zhang Z-K, Zhou T. Recommender systems. Phys Rep. 2012;519(1):1–49.

    Article  Google Scholar 

  5. 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. Br J Sports Med. 2022;17:1–7.

    Google Scholar 

  6. Greenland S, Pearl J, Robins JM. Confounding and collapsibility in causal inference. Stat Sci. 1999;14(1):29–46.

    Article  Google Scholar 

  7. 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 Orthopaed Sports Phys Ther. 2021;51(10):517–25.

    Article  Google Scholar 

  8. Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why, and how? BMJ. 2009;23:338.

    Google Scholar 

  9. Meeuwisse WH, Tyreman H, Hagel B, Emery C. A dynamic model of etiology in sport injury: the recursive nature of risk and causation. Clin J Sport Med. 2007;17(3):215–9.

    Article  Google Scholar 

  10. Shanley E, Thigpen CA, Collins GS, Arden NK, Noonan TJ, Wyland DJ, et al. Including modifiable and non-modifiable factors improves injury risk assessment in professional baseball pitchers. J Orthop Sports Phys Ther. 2022;52(9):630–40.

    Article  Google Scholar 

  11. Stovitz SD, Verhagen E, Shrier I. Distinguishing between causal and non-causal associations: implications for sports medicine clinicians. Br J Sports Med. 2019;53(7):398–9.

    Article  Google Scholar 

  12. Christodoulou E, Ma J, Collins GS, Steyerberg EW, Verbakel JY, Van Calster B. A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models. J Clin Epidemiol. 2019;110:12–22.

    Article  Google Scholar 

  13. 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. Sports Med. 2022;52(10):2469–82.

    Article  Google Scholar 

  14. Royston P, Sauerbrei W. A new approach to modelling interactions between treatment and continuous covariates in clinical trials by using fractional polynomials. Stat Med. 2004;23(16):2509–25.

    Article  Google Scholar 

  15. Durrleman S, Simon R. Flexible regression models with cubic splines. Stat Med. 1989;8(5):551–61.

    Article  CAS  Google Scholar 

  16. van der Ploeg T, Austin PC, Steyerberg EW. Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints. BMC Med Res Methodol. 2014;14(1):1–13.

    Google Scholar 

  17. Hand DJ. Classifier technology and the illusion of progress. Stat Sci. 2006;21(1):1–14.

    Google Scholar 

  18. Mitchell TM. Does machine learning really work? AI Mag. 1997;18(3):11.

    Google Scholar 

  19. Austin PC, Harrell FE, Lee DS, Steyerberg EW. Empirical analyses and simulations showed that different machine and statistical learning methods had differing performance for predicting blood pressure. Sci Rep. 2022;12(1):1–11.

    Article  Google Scholar 

  20. Austin PC, Harrell FE Jr, Steyerberg EW. Predictive performance of machine and statistical learning methods: impact of data-generating processes on external validity in the “large N, small p” setting. Stat Methods Med Res. 2021;30(6):1465–83.

    Article  Google Scholar 

  21. Rubin DB. An overview of multiple imputation. In: Proceedings of the survey research methods section of the American Statistical Association. Citeseer. 1988. p. 79–84.

  22. Rubin DB. Multiple imputation after 18+ years. J Am Stat Assoc. 1996;91(434):473–89.

    Article  Google Scholar 

  23. Rubin DB, Schenker N. Multiple imputation in health-are databases: an overview and some applications. Stat Med. 1991;10(4):585–98.

    Article  CAS  Google Scholar 

  24. Harrell FE. Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis. Berlin: Springer; 2001.

    Book  Google Scholar 

  25. van den Goorbergh R, van Smeden M, Timmerman D, Van Calster B. The harm of class imbalance corrections for risk prediction models: illustration and simulation using logistic regression. J Am Med Inform Assoc. 2022;29(9):1525–34.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Orthopaedic Surgery and Rehabilitation, Wake Forest School of Medicine, 475 Vine St., Winston-Salem, NC, 27101, USA

    Garrett S. Bullock

  2. Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, UK

    Garrett S. Bullock & Stefan Kluzek

  3. Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK

    Garrett S. Bullock, Gary S. Collins & Stefan Kluzek

  4. Manchester United Football Club, Manchester, UK

    Tom Hughes

  5. Department of Health Professions, Manchester Metropolitan University, Manchester, UK

    Tom Hughes

  6. Red Bull Athlete Performance Center, Thalgua, Austria

    Amelia A. J. H. Arundale

  7. Icahn School of Medicine, Mount Sinai Health System, New York, NY, USA

    Amelia A. J. H. Arundale

  8. Seattle Seahawks, Seattle, WA, USA

    Patrick Ward

  9. Centre for Statistics in Medicine, University of Oxford, Oxford, UK

    Gary S. Collins

  10. Oxford University Hospitals NHS Foundation Trust, Oxford, UK

    Gary S. Collins

  11. University of Nottingham, Nottingham, UK

    Stefan Kluzek

Authors
  1. Garrett S. Bullock
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Tom Hughes
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Amelia A. J. H. Arundale
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Patrick Ward
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gary S. Collins
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Stefan Kluzek
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Garrett S. Bullock.

Ethics declarations

Funding

No sources of funding were used to assist in the preparation of this letter.

Conflicts of interest

Garrett Bullock, Amelia Arundale, Patrick Ward, Gary Collins, and Stefan Kluzek have no conflicts of interest that are directly relevant to the content of this letter. Tom Hughes has spoken at a conference and online seminar organized by Kitman Labs, with both lectures based on the original publication. He has not received payment for this, although he received travel expenses for attendance at the conference. He has no other conflicts of interest to declare.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bullock, G.S., Hughes, T., Arundale, A.A.J.H. et al. Response to Comment on: “Black Box Prediction Methods in Sports Medicine Deserve a Red Card for Reckless Practice: A Change of Tactics is Needed to Advance Athlete Care”. Sports Med 53, 297–299 (2023). https://doi.org/10.1007/s40279-022-01771-3

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40279-022-01771-3

Search

Navigation