A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning.

All hypothesis tests were conducted using two-sided hypothesis tests, setting α in the hypothesis test to 0.05 and considering p > 0.1 as not significant, p < 0.1 as marginally significant, p < 0.05 as significant and p < 0.01 as highly significant.

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When there was a risk of mild LENCI, the athletes’ perceived well-being indices showed a trend of deterioration, consistent with previous findings.

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