Descriptive epidemiology of orthopedic injury and illness during the Special Olympics of Pennsylvania Summer Games from 2008 to 2017
Bibliographic record
Abstract
Background: The Special Olympics Pennsylvania Summer Games attract over 2000 athletes each year. Volunteer medical staff ensures their safety throughout this period. However, few studies have examined the incidence of orthopedic injury and sickness in this group, especially with a large sample. Objective: Identify the incidence of orthopedic injury and Illness at the Special Olympics Pennsylvania Summer Games based on demographic criteria and identify the incidence of transports required for advanced care. Methods: Data was collected from logs provided by Special Olympics Pennsylvania. The data were analyzed and stratified by gender, age, sport, and type of encounter. We summarized the data and compared it to data from other years and the average. Results: An average of 1971 athletes competed annually. On average, 10% (N=144) of competitors required medical care. Males comprised 58.2% (N = 837) of encounters, females 33.6% (N = 483), and in 8.1% (N = 117) of encounters gender was not identified/recorded. The mean age of participants was 29 years of age (range from 10 to 83). 56.6% (N= 813) of encounters required first aid management only. Injuries made up 31.7% (N = 455) of total encounters, and 11.8% (N=169) of encounters were classified as illnesses. Basketball was the sport with the most injuries, 49.5% (N = 711). An average of 9.8 transports was required annually. Conclusions: Special Olympics athletes suffer the same injuries as regular athletes, but they are also prone to various medical disorders that regular athletes are not.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.003 | 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".