External ValidationResearch

Testing whether a study's findings hold up in different people, places, or settings.

External validation refers to evaluating a prediction model, diagnostic tool, or research finding using a dataset or population separate from the one it was developed on — assessing whether results generalise beyond the original study sample. This differs from internal validation (e.g. cross-validation or bootstrapping within the same dataset), which checks reproducibility but not generalisability. A model can perform well internally yet fail external validation if the new population differs in demographics, setting, or measurement methods, so clinicians should weigh how similar their own patients are to the validation cohort before applying the tool.

• Effect of Time Since Baseline Data Collection, Mechanism of Injury, and Service Academy Sub-population on Concussion Risk Prediction in Military Service Academy Cadets and Midshipmen: An Observational Study · issue 001
• Identifying rotator cuff disorders through shoulder movement quality: An exploratory study using a single inertial measurement unit · issue 002
• Association of Persistent Interlimb Kinetic Asymmetry at Return to Sport With Increased Risk of Contralateral Anterior Cruciate Ligament Injury · issue 006
• Personalizing the Quadriceps Tendon Graft in Anterior Cruciate Ligament Reconstruction: A Novel, Continuous Method to Predict Graft Size Using Preoperative MRI. · issue 007

This guide was auto-drafted and is pending editorial review.