Factors Influencing Safe Return-to-Play Recommendations Following Sports Injuries Assessed in Urgent Care Centers
Bibliographic record
Abstract
Introduction: Significant focus has been directed toward the alarming incidence rates of sports injuries and traumatic injuries associated with sports participation, particularly in the pediatric and young adult population. Anterior cruciate ligament and meniscal injuries occur in nearly 40 per 100,000 adolescents aged 5–18. From 2009 to 2010, approximately 2.6 million cases were treated in emergency departments or urgent care centers among the 29.2 million pediatric patients involved in sports and recreational activities. The ramifications of these injuries have long-term financial and functional impacts on the affected athletes. Patients are at a seven-fold greater risk of sustaining a second ACL injury, and 70% develop knee osteoarthritis as early as 10 to 15 years post-injury. Amendments to the provisions of athlete safety, such as the passage of return-to-play laws in all 50 United States, encourage comprehensive protocols that protect growing athletes from premature return to play after sustaining sport-related concussion injuries. However, limitations with the content and enforcement of these laws, along with societal pressures and mixed messaging, factor into the decision-making process for optimal management and safe return to play following all sports injuries. Methods: Here we present additional methods aimed at helping readers understand the study data, with particular emphasis on study methods, detailed statistical approaches, and processes specific to the determination of the dependent variables. This study is part of a larger project with the aim of observing TBI and other sports injuries in UCs in the Western Swedish County during high seasons for different sports and describing recovery progress. To comply with imperative guidelines for handling personal information, data from subtype injuries (other than TBI) were not used. Data collection occurred from 2014-2018 using a smartphone app for reporting visits regarding specific injuries. The app had a minimal impact and was implemented through briefings and regular reminders. Non-identifiable data from the Electronic Health Records were collected at the outpatient clinic. Foster visits were added for injuries with progress assessments. Conclusion: In conclusion, RICE and other factors influenced whether injury victims were advised to stop play or return to play at this check-in point. Specific sports injuries may benefit from the restriction of some activities while undergoing more examination before making sport-specific recommendations. Reliable and valid continuing education may achieve best practices through peer behavior modeling. This could be disseminated to all providers of care for otherwise healthy sport participants. Delineation of which healthcare providers sport participants seek out, the conditions they want to know about, and their readiness to learn in order to better advise sport participants on a healthy return to play should be examined. The advisability of follow-up care in a specialty sports treatment facility or with a specialist in these injuries, the use of telehealth, and other issues should be explored in future studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".