UNASSIGNED: There is a lack of studies of alternative techniques differing from the straight leg raise test (SLR) and the passive knee extension test ( @entity68953 ) to diagnose @entity851 ( @entity851 ). We built a predictive model with simple parameters to diagnose @entity851 and implemented it in a mobile app. This cross-sectional study analyzed 85 Spanish @entity1 aged 10-16 years who played soccer in 2012. OUTCOMES: @entity851 (SLR<70   and/or @entity68953 >15  ), and grade II @entity851 (SLR<60   and/or @entity68953  ). Secondary variables: toe-touch test (TT), body mass index (BMI), age, laterality and number of years registered as part of a federation. A risk table implemented in a mobile app was built to estimate the probability of @entity851 and grade II @entity851 according to secondary variables. The area under the ROC curve (AUC) was calculated and we constructed risk groups. Scoring factors for @entity851 : low TT, younger age and lower BMI. AUC: 0.89 (95% CI: 0.82-0.96, p<0.001). Scoring factors for grade II @entity851 : younger age, higher BMI, left footed and lower TT. AUC: 0.78 (95% CI: 0.68-0.88, p<0.001). We provide a tool with minimum material but with a high discriminatory power to quickly calculate whether a @entity1 who plays soccer has @entity851 . The models need validation studies.

A Tool to Quickly Detect Short Hamstring Syndrome in XXXX who Play Soccer.

@entity1 :: ('9606', 'Species') :: ['boys', 'boy'] @entity68953 :: ('282974', 'Gene') :: ['PKE>= 35', 'PKE'] @entity851 :: ('MESH:D006130', 'Disease') :: ['short hamstring syndrome', 'SHS']
@entity1 :: (9606,Species) :: ['Boys']
