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Methods matter: clinical prediction models will benefit sports medicine practice, but only if they are properly developed and validated
  1. Garrett S Bullock1,2,
  2. Tom Hughes3,4,
  3. Jamie C Sergeant5,6,
  4. Michael J Callaghan3,4,5,
  5. Richard Riley7,
  6. Gary Collins8,9
  1. 1Centre for Sport, Exercise and Osteoarthritis Research Versus Arthritis, University of Oxford, Oxford, UK
  2. 2Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK
  3. 3Manchester United Football Club, AON Training Complex, Manchester, UK
  4. 4Department of Health Professions, Manchester Metropolitan University, Manchester, UK
  5. 5Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
  6. 6Centre for Biostatistics, Manchester Academic Health Science Centre, University of Manchester, Manchester, UK
  7. 7Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK
  8. 8Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
  9. 9Oxford University Hospitals NHS Foundation Trust, Oxford, UK
  1. Correspondence to Dr Garrett S Bullock, Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, Oxfordshire, UK; garrettbullock{at}gmail.com

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Introduction

Sports medicine clinicians are expected to make accurate diagnoses, estimate prognoses and identify athletes at risk of sustaining an injury.1 These complex decisions are dependent on clinical reasoning, which is informed by, and often biased toward, a practitioner’s scientific knowledge and experience. Clinical prediction models are developed by researchers to help facilitate such decisions in practice2; data for multiple predictor variables are combined to estimate an individual’s risk of a health outcome either being present (diagnosis) or whether it will occur in future (prognosis).3 Despite being employed widely in clinical medicine, clinical prediction models are uncommon in sports medicine. Clinical prediction models can offer benefits to both practitioners and athletes, but only if they are developed and validated using rigorous methods and transparently reported so that potential users can judge their accuracy and usefulness.

Therefore, the purpose of this editorial is to describe the recommended steps for clinical prediction development and validation and to guide practitioners using and interpreting prediction models in sports medicine.

Model development

The first step in developing a prediction model is to identify its clinical need, the target population and how and when it would fit into the clinical workflow. Models should predict outcomes that are relevant to sport stakeholders, and be clearly defined, including how and when assessed.4

Next is to identify any existing models that could be evaluated or updated. If not, …

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Footnotes

  • GSB and TH are joint first authors.

  • Twitter @DRGSBullock, @dt_hughes

  • Correction notice This article has been corrected since it published Online First. The author affiliations have been corrected.

  • Contributors GSB, TH, JCS, MJC, RR and GC conceived the study idea. GSB, TH, JCS, MJC, RR and GSC were involved in design and planning. GSB, TH, RR and GC wrote the first draft of the manuscript. GSB, TH, JCS, MJC, RR and GC critically revised the manuscript. GSB, TH, JCS, MJC, RR and GC approved the final version of the manuscript.

  • Funding GSC was supported by the NIHR Biomedical Research Centre, Oxford, and Cancer Research UK (programme grant: C49297/A27294).

  • Competing interests None declared.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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