{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"OrthoDigest","title":"Episode 98: Sports Medicine Edition — Vol. 1, Issue 14 (2026-04-12)","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/2c07a218\"></iframe>","width":"100%","height":180,"duration":718,"description":"Today's sports medicine edition covers four diverse topics in athletic injury and recovery. We explore wearable sensor technology for objective gait assessment during ACL rehabilitation, examine the psychological readiness of elite judokas returning to sport after injury, investigate machine learning approaches to predict rotator cuff tears using anatomical parameters, and compare tourniquet types in ACL reconstruction surgery.\n\n\n\"Integrating Wearable Sensors and Clinical Tools for Assessing Pelvic Gait Symmetry During ACL Recovery\" — Drumev AK et al., Life (Basel) — https://doi.org/10.3390/life16030531\n\"Kinesiophobia and Psychological Readiness of Return to Sport in High-Performance Judokas After an Injury: A Cross-Sectional Study\" — Puchalt-Muñoz U et al., Medicina (Kaunas) — https://doi.org/10.3390/medicina62030587\n\"Machine learning-based prediction of rotator cuff tears using anatomical parameters: a retrospective cohort study\" — Hou Z et al., BMC Musculoskelet Disord — https://doi.org/10.1186/s12891-026-09765-2\n\"The effect of silicone ring tourniquet in anterior cruciate ligament reconstruction: a retrospective comparative study\" — Shen X et al., BMC Musculoskelet Disord — https://doi.org/10.1186/s12891-026-09732-x","thumbnail_url":"https://img.transistorcdn.com/Fy3Dok22tN2ECwQqF8pWKhSMfGyY8UtHppU1XPNNv4c/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS8wNjFj/NmM4NmMxMzVhM2Qw/N2IyMjA0YzE1MGFh/MWFiYy5wbmc.webp","thumbnail_width":300,"thumbnail_height":300}