To develop a machine learning-ready dataset for predicting running-related injuries (RRI) using a variety of risk factors from multiple disciplines.
Key Findings:
Moderate improvement in prediction performance (AUC = 0.784 ± 0.014) compared to previous models, indicating a better predictive capability.
Random forest algorithm achieved the highest performance (AUC = 0.781 ± 0.016, 0.784 ± 0.014).
Logistic regression showed significant improvement when trained with a broader range of risk factors.
Interpretation:
The study provides a reproducible framework for future machine learning research in sports injury prediction, emphasizing the importance of integrating diverse risk factors to enhance predictive accuracy.
Limitations:
The dataset is limited to competitive endurance runners, which may affect generalizability to other populations.
Some metrics need to remain normalized to ensure participant anonymity, potentially limiting data richness.
Conclusion:
This research contributes valuable insights and a dataset for advancing machine learning applications in predicting running-related injuries.
A practical, age-appropriate treadmill protocol outlines how clinicians can safely assess exercise intolerance and guide concussion management in children aged 5 to 12 years.
Researchers evaluated perioperative and postdischarge factors associated with opioid refill prescriptions during the first 90 days after inpatient otolaryngology–head and neck surgery.
The nurse practitioner profession claims the No. 1 spot across three categories in the U.S. News & World Report 2026 Best Jobs rankings for the third consecutive year.
