Cheerleading injuries in children: What can be learned?
Bibliographic record
Abstract
INTRODUCTION: Cheerleading has gradually become more popular in Canada and represents an accessible way for youth to be physically active. OBJECTIVE: To determine the differences in the injuries encountered by cheerleaders according to their age, in order to propose safety guidelines that take into account the developmental stages of children. METHOD: Retrospective database review of cheerleading injuries extracted from the Canadian Hospitals Injury Reporting and Prevention Program (CHIRPP) database between 1990 and 2010. The injuries were compared by age group (5 to 11 versus 12 to 19) according to their sex, mechanism of injury and injury severity. RESULTS: Overall, in 20 years, there were 1496 cases of injuries documented secondary to cheerleading (median age 15, 4 (interquartile range [IQR]=2, 2) years); mostly females (1410 [94%]). Of that number, 101 cases were 5 to 11 years old (age group [AG]1), while 1385 were 12 to 19 (AG2). Participants in AG1 were found to have a higher proportion of moderate-to-severe injury (46.5% compared with 28.2% in AG2). The odds ratio of moderate/severe injury for AG1 compared with AG2 was found to be 2.217 (95% CI [1.472; 3.339]). No fatalities were known to have occurred. CONCLUSION: Children's developmental stages affect their ability to participate in sports and the responses of their bodies to impact forces. Our findings concerning cheerleading injuries indicate that younger children (5 to 11 years old) are more likely to suffer moderate-to-severe injuries. Thus, on a local basis, the use of appropriate safety measures including appropriate flooring/safety mats and spotters to catch falling athletes should be mandatory.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".