Limitations of Separating Athletes into High or Low-Risk Groups based on a Cut-Off. A Clinical Commentary.
Justin M Losciale, Linda K Truong, Patrick Ward and 2 others
PMID 39229450WHAT IT FOUND
Splitting athletes into high or low risk groups using cut-offs like 90% strength symmetry hides the true relationship between test scores and injury.
It discards data, inflates errors, and prevents personalised care. Track continuous values instead to make better decisions.
Key findings
01Using cut-offs to split continuous data assumes a false biological dichotomy, treating athletes with 50% and 87% strength symmetry as having identical injury risk.
02Prediction models using continuous variables in their natural form are significantly more accurate than those using cut-offs, correctly identifying 47 more injured individuals per 100 screened.
03Cut-offs hinder personalised care because decisions become driven by group membership rather than the individual athlete's specific values, risk tolerance, or context.
STILL TO COME
How it was doneWhat they foundWhat it means for PTs
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What it does not show
This is an expert opinion piece and does not present new primary data or a systematic review of evidence. The recommendations challenge widely accepted clinical protocols, which may require significant changes in practice and patient education. The commentary acknowledges that some cut-offs are still useful for rapid triage in emergency situations, such as cervical fracture screening.
Declared interests
The authors declare no conflicts of interest. One author is funded by the Arthritis Society and the University of British Columbia, and another by the Canadian Institutes of Health Research.
The easy way to misread this
Do not interpret this commentary as a recommendation to abandon all clinical guidelines or decision rules. The authors specify that cut-offs remain valuable for rapid triage in serious conditions, such as using the Canadian Cervical Spine Rule to decide on radiography, where missing a fracture is far more dangerous than a false positive.