{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T07:57:25Z","timestamp":1793174245300,"version":"4.5.0"},"reference-count":37,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2013,3,20]],"date-time":"2013-03-20T00:00:00Z","timestamp":1363737600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Scandinavian Med Sci Sports"],"published-print":{"date-parts":[[2013,8]]},"abstract":"<jats:p>In athletics, efficient screening tools are sought to curb the rising number of noncontact injuries and associated health care costs. The authors hypothesized that an injury prediction algorithm that incorporates movement screening performance, demographic information, and injury history can accurately categorize risk of noncontact lower extremity (<jats:styled-content style=\"fixed-case\">LE<\/jats:styled-content>) injury. One hundred eighty\u2010three collegiate athletes were screened during the preseason. The test scores and demographic information were entered into an injury prediction algorithm that weighted the evidence\u2010based risk factors. Athletes were then prospectively followed for noncontact <jats:styled-content style=\"fixed-case\">LE<\/jats:styled-content> injury. Subsequent analysis collapsed the groupings into two risk categories: Low (normal and slight) and High (moderate and substantial). Using these groups and noncontact <jats:styled-content style=\"fixed-case\">LE<\/jats:styled-content> injuries, relative risk (RR), sensitivity, specificity, and likelihood ratios were calculated. Forty\u2010two subjects sustained a noncontact <jats:styled-content style=\"fixed-case\">LE<\/jats:styled-content> injury over the course of the study. Athletes identified as High Risk (<jats:italic>n<\/jats:italic>\u2009=\u200963) were at a greater risk of noncontact <jats:styled-content style=\"fixed-case\">LE<\/jats:styled-content> injury (27\/63) during the season [<jats:styled-content style=\"fixed-case\">RR<\/jats:styled-content>: 3.4 95% confidence interval 2.0 to 6.0]. These results suggest that an injury prediction algorithm composed of performance on efficient, low\u2010cost, field\u2010ready tests can help identify individuals at elevated risk of noncontact <jats:styled-content style=\"fixed-case\">LE<\/jats:styled-content> injury.<\/jats:p>","DOI":"10.1111\/sms.12062","type":"journal-article","created":{"date-parts":[[2013,3,21]],"date-time":"2013-03-21T02:52:28Z","timestamp":1363834348000},"source":"Crossref","is-referenced-by-count":81,"title":["Field\u2010expedient screening and injury risk algorithm categories as predictors of noncontact lower extremity injury"],"prefix":"10.1111","volume":"23","author":[{"given":"M. E.","family":"Lehr","sequence":"first","affiliation":[{"name":"Department of Physical Therapy Lebanon Valley College  Annville Pennsylvania USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"P. 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