Injuries to the hamstring muscle complex are common in athletes, accounting for between 12% and 26% of all injuries sustained during sporting activities. Acute hamstring injuries often occur during sports that involve repetitive kicking or high-speed sprinting, such as American football, soccer, rugby, and athletics. They are also common in watersports, including waterskiing and surfing. Hamstring injuries can be career-threatening in elite athletes and are associated with an estimated risk of recurrence in between 14% and 63% of patients. The variability in prognosis and treatment of the different injury patterns highlights the importance of prompt diagnosis with magnetic resonance imaging (MRI) in order to classify injuries accurately and plan the appropriate management. Low-grade hamstring injuries may be treated with nonoperative measures including pain relief, eccentric lengthening exercises, and a graduated return to sport-specific activities. Nonoperative management is associated with highly variable times for convalescence and return to a pre-injury level of sporting function. Nonoperative management of high-grade hamstring injuries is associated with poor return to baseline function, residual muscle weakness and a high-risk of recurrence. Proximal hamstring avulsion injuries, high-grade musculotendinous tears, and chronic injuries with persistent weakness or functional compromise require surgical repair to enable return to a pre-injury level of sporting function and minimize the risk of recurrent injury. This article reviews the optimal diagnostic imaging methods and common classification systems used to guide the treatment of hamstring injuries. In addition, the indications and outcomes for both nonoperative and operative treatment are analyzed to provide an evidence-based management framework for these patients. Cite this article:
‘Big data’ is a term for data sets that are so
large or complex that traditional data processing applications are
inadequate. Billions of dollars have been spent on attempts to build predictive
tools from large sets of poorly controlled healthcare metadata.
Companies often sell reports at a physician or facility level based
on various flawed data sources, and comparative websites of ‘publicly
reported data’ purport to educate the public. Physicians should
be aware of concerns and pitfalls seen in such data definitions,
data clarity, data relevance, data sources and data cleaning when
evaluating analytic reports from metadata in health care. Cite this article:
