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Objective: To provide athletic trainers with a fundamental understanding of the pathogenesis and risk factors associated with the development of posttraumatic osteoarthritis (PTOA) as well as the best current recommendations for preventing and managing this condition. Background: Posttraumatic osteoarthritis, or osteoarthritis that develops secondary to joint injury, accounts for approximately 5.5 million US cases annually. A young athlete with a joint injury is at high risk for PTOA before the age of 40, which could lead to the patient living more than half of his or her life with a painful and disabling disorder. Given our frequent contact with physically active people who often sustain traumatic joint injuries, athletic trainers are in a unique position to help prevent and manage PTOA. We can, therefore, regularly monitor joint health in at-risk patients and implement early therapies as necessary. Recommendations: The recommendations for preventing and managing PTOA are based on the best available evidence. Primary injury prevention, self-management strategies, maintenance of a healthy body weight, and an appropriate level of physical activity should be encouraged among those at risk for PTOA after acute traumatic joint injury. Education of athletic trainers and patients regarding PTOA is also critical for effective prevention and management of this disease. [ABSTRACT FROM AUTHOR]

Context: Women are 2 to 8 times more likely to sustain an anterior cruciate ligament (ACL) injury than men, and previous studies indicated an increased risk for injury during the preovulatory phase of the menstrual cycle (MC). However, investigations of risk rely on retrospective classification of MC phase, and no tools for this have been validated. Objective: To evaluate the accuracy of an algorithm for retrospectively classifying MC phase at the time of a mock injury based on MC history and salivary progesterone (P4) concentration. Design: Descriptive laboratory study. Setting: Research laboratory. Participants: Thirty-one healthy female collegiate athletes (age range, 18-24 years) provided serum or saliva (or both) samples at 8 visits over 1 complete MC. Main Outcome Measure(s): Self-reported MC information was obtained on a randomized date (1-45 days) after mock injury, which is the typical timeframe in which researchers have access to ACL-injured study participants. The MC phase was classified using the algorithm as applied in a stand-alone computational fashion and also by 4 clinical experts using the algorithm and additional subjective hormonal history information to help inform their decision. To assess algorithm accuracy, phase classifications were compared with the actual MC phase at the time of mock injury (ascertained using urinary luteinizing hormone tests and serial serum P4 samples). Clinical expert and computed classifications were compared using κ statistics. Results: Fourteen participants (45%) experienced anovulatory cycles. The algorithm correctly classified MC phase for 23 participants (74%): 22 (76%) of 29 who were preovulatory/ anovulatory and 1 (50%) of 2 who were postovulatory. Agreement between expert and algorithm classifications ranged from 80.6% (κ = 0.50) to 93% (κ = 0.83). Classifications based on same-day saliva sample and optimal P4 threshold were the same as those based on MC history alone (87.1% correct). Algorithm accuracy varied during the MC but at no time were both sensitivity and specificity levels acceptable. Conclusions: These findings raise concerns about the accuracy of previous retrospective MC-phase classification systems, particularly in a population with a high occurrence of anovulatory cycles. [ABSTRACT FROM AUTHOR]

The article discusses the risk factor for anterior cruciate ligament (ACL) injuries associated with posttraumatic arthritis. It states that the ACL injury produce both shortterm and long-term changes in knee-joint geometry. It also cites the increase risk of sustaining an ACL injury among athletes based on the level of sport competition.