Cincinnati Children's Hospital Medical Center, and Department of Pediatrics, College of Medicine, University of Cincinnati, 3333 Burnet Avenue, MLC 10001, Cincinnati, OH 45229, USA.
Br J Sports Med. 2011 Apr;45(4):238-44. doi: 10.1136/bjsm.2010.072843. Epub 2010 Nov 16.
High knee abduction moment (KAM) landing mechanics, measured in the biomechanics laboratory, can successfully identify female athletes at increased risk for anterior cruciate ligament (ACL) injury.
The authors validated a simpler, clinic-based ACL injury prediction algorithm to identify female athletes with high KAM measures. The validated ACL injury prediction algorithm employs the clinically obtainable measures of knee valgus motion, knee flexion range of motion, body mass, tibia length and quadriceps-to-hamstrings ratio. It predicts high KAMs in female athletes with high sensitivity (77%) and specificity (71%).
This report outlines the techniques for this ACL injury prediction algorithm using clinic-based measurements and computer analyses that require only freely available public domain software.
在生物力学实验室中测量的高膝外展力矩（KAM）着陆力学，可以成功识别出前交叉韧带（ACL）受伤风险增加的女性运动员。
作者验证了一种更简单的基于临床的 ACL 损伤预测算法，以识别出 KAM 测量值较高的女性运动员。经过验证的 ACL 损伤预测算法采用临床上可获得的膝关节外翻运动、膝关节活动范围、体重、胫骨长度和股四头肌与腘绳肌比例等指标来预测女性运动员的高 KAM。该算法具有较高的敏感性（77%）和特异性（71%）。
本报告概述了使用基于临床的测量值和计算机分析的 ACL 损伤预测算法的技术，这些技术仅需要免费提供的公共领域软件。
