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New method to identify athletes at high risk of ACL injury using clinic-based measurements and freeware computer analysis.
MedLine Citation:
PMID:  21081640     Owner:  NLM     Status:  In-Data-Review    
Abstract/OtherAbstract:
Background 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. Methods 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%). Conclusion 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.
Authors:
Gregory D Myer; Kevin R Ford; Timothy E Hewett
Publication Detail:
Type:  Journal Article     Date:  2010-11-16
Journal Detail:
Title:  British journal of sports medicine     Volume:  45     ISSN:  1473-0480     ISO Abbreviation:  Br J Sports Med     Publication Date:  2011 Apr 
Date Detail:
Created Date:  2011-03-29     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  0432520     Medline TA:  Br J Sports Med     Country:  England    
Other Details:
Languages:  eng     Pagination:  238-44     Citation Subset:  IM    
Affiliation:
Cincinnati Children's Hospital, 3333 Burnet Avenue, MLC 10001, Cincinnati, OH 45229, USA. greg.myer@cchmc.org.
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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