Development of Machine-Learning Algorithms to Predict Attainment of Minimal Clinically Important Difference After Hip Arthroscopy for Femoroacetabular Impingement Yield Fair Performance and Limited Clinical Utility.

Purpose: To determine whether machine learning (ML) techniques developed using registry data could predict which patients will achieve minimum clinically important difference (MCID) on the International Hip Outcome Tool 12 (iHOT-12) patient-reported outcome measures (PROMs) after arthroscopic management of femoroacetabular impingement syndrome (FAIS). And secondly to determine which preoperative factors contribute to the predictive power of these models.

Methods: A retrospective cohort of patients was selected from the UK's Non-Arthroplasty Hip Registry.

Measurement Techniques for Leg Length Discrepancy in Total Hip Arthroplasty: A Systematic Review of Reliability and Validity.

Background: Total hip arthroplasty (THA) carries a substantial litigative burden. THA may introduce leg length discrepancy (LLD), necessitating a valid and reliable technique for LLD measurement. This study investigates the reliability and validity of techniques quantitively measuring LLD in both pre- and post-THA.