Time to return to full training is delayed and recurrence rate is higher in intratendinous ('c') acute hamstring injury in elite track and field athletes: clinical application of the British Athletics Muscle Injury Classification.

BACKGROUND: The British Athletics Muscle Injury Classification describes acute muscle injuries and their anatomical site within muscle based on MRI parameters of injury extent. It grades injuries from 0 to 4 and classifies location based on a myofascial (a), musculotendinous (b) or intratendinous (c) description. This is a retrospective cohort study that assessed time to return to full training (TRFT) and injury recurrence in the different British Athletics classifications for hamstring injuries sustained by elite track and field (T&F) athletes over a 4-year period. METHODS: The electronic medical records (EMRs) of 230 elite British T&F athletes were reviewed. Athletes who sustained an acute hamstring injury, with MRI investigation within 7 days of injury, were included. MRI were graded by two musculoskeletal radiologists using the British Athletics Muscle Injury Classification. The EMRs were reviewed by 2 sports physicians, blinded to the new classification; TRFT and injury recurrence were recorded. RESULTS: There were 65 hamstring injuries in 44 athletes (24±4.4 years; 28 male, 16 female). TRFT differed among grades (p<0.001). Grade 3 injuries and 'c' injuries took significantly longer and grade 0 injuries took less TRFT. There were 12 re-injuries; the injury recurrence rate was significantly higher in intratendinous (c) injuries (p<0.001). There was no difference in re-injury rate between number grades 1-3, hamstring muscle affected, location (proximal vs central vs distal), age or sex. CONCLUSIONS: This study describes the clinical application of the British Athletics Muscle Injury Classification. Different categories of hamstring injuries had different TRFT and recurrence rate. Hamstring injuries that extend into the tendon ('c') are more prone to re-injury and delay TRFT.

k-Neighborhood decentralization: a comprehensive solution to index the UMLS for large scale knowledge discovery.

The Unified Medical Language System (UMLS) is the largest thesaurus in the biomedical informatics domain. Previous works have shown that knowledge constructs comprised of transitively-associated UMLS concepts are effective for discovering potentially novel biomedical hypotheses. However, the extremely large size of the UMLS becomes a major challenge for these applications. To address this problem, we designed a k-neighborhood Decentralization Labeling Scheme (kDLS) for the UMLS, and the corresponding method to effectively evaluate the kDLS indexing results. kDLS provides a comprehensive solution for indexing the UMLS for very efficient large scale knowledge discovery. We demonstrated that it is highly effective to use kDLS paths to prioritize disease-gene relations across the whole genome, with extremely high fold-enrichment values. To our knowledge, this is the first indexing scheme capable of supporting efficient large scale knowledge discovery on the UMLS as a whole. Our expectation is that kDLS will become a vital engine for retrieving information and generating hypotheses from the UMLS for future medical informatics applications.