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

UNLABELLED: There is a lack of studies of alternative techniques differing from the straight leg raise test (SLR) and the passive knee extension test (PKE) to diagnose short hamstring syndrome (SHS). We built a predictive model with simple parameters to diagnose SHS and implemented it in a mobile app. This cross-sectional study analyzed 85 Spanish boys aged 10-16 years who played soccer in 2012. OUTCOMES: SHS (SLR<70° and/or PKE>15°), and grade II SHS (SLR<60° and/or PKE≥35°). 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 SHS and grade II SHS according to secondary variables. The area under the ROC curve (AUC) was calculated and we constructed risk groups. Scoring factors for SHS: low TT, younger age and lower BMI. AUC: 0.89 (95% CI: 0.82-0.96, p<0.001). Scoring factors for grade II SHS: 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 boy who plays soccer has SHS. The models need validation studies.

Differences in the management of hypertension, diabetes mellitus and dyslipidemia between obesity classes.

We did not find any paper that assessed clinical inertia in obese patients. Therefore, no paper has compared the clinical inertia rates between morbidly and nonmorbidly obese patients. A cross-sectional observational study was carried out. We analysed 8687 obese patients ⩾40 years of age who attended their health-care center for a checkup as part of a preventive program. The outcome was morbid obesity. Secondary variables were as follows: failure in the management of high blood pressure (HBP), high blood cholesterol (HBC) and high fasting blood glucose (HFBG); gender; personal history of hypertension, dyslipidemia, diabetes, smoking and cardiovascular disease; and age (years). We analysed the association between failures and morbid obesity by calculating the adjusted odds ratio (OR). Of 8687 obese patients, 421 had morbid obesity (4.8%, 95% confidence interval (CI): 4.4-5.3%). The prevalence rates for failures were as follows: HBP, 34.7%; HBC, 35.2%; and HFBG, 12.4%. Associated factors with morbid obesity related with failures were as follows: failure in the management of HBP (OR=1.42, 95% CI: 1.15-1.74, P=0.001); failure in the management of HBC (OR=0.73, 95% CI: 0.58-0.91, P=0.004); and failure in the management of HFBG (OR=2.24, 95% CI: 1.66-3.03, P<0.001). Morbidly obese patients faced worse management for HBP and HFBG, and better management for HBC. It would be interesting to integrate alarm systems to avoid this problem.