Epidemiology of Acute Soccer Injuries in Canadian Children and Youth
Bibliographic record
Abstract
OBJECTIVES: To describe acute injury characteristics in children and youth soccer players and to identify the characteristics of patients who required hospital admission. METHODS: The analysis of the study was based on the Canadian Hospitals Injury Reporting and Prevention Program. A total of 32,149 patients (aged 5-19 years) with soccer-related injuries presenting to 16 participating hospital emergency departments from 1994 to 2004 were included in the analysis. RESULTS: Males had the highest proportion of injuries (62%). The leading injuries were sprains/strains (38%), followed by fractures/dislocations (31%) and superficial injuries (23%). A total of 896 cases (3%) required hospital admission. Based on logistic regression analysis, being a male, playing unorganized soccer, having multiple body injuries, playing soccer outside school premises, and playing during the summer/fall increased the likelihood of hospital admission. Moreover, having a head/face/neck injury (Odds ratio [OR], 1.3; 95% confidence interval [95% CI], 1.1-1.7) and trunk injury (OR, 1.7; 95% CI, 1.2-2.4) as compared with an upper extremity injury and having injuries from contact with structures/surfaces (OR, 3.1; 95% CI, 2.2-4.3) and with other players (OR, 2.5; 95% CI, 1.8-3.5) as compared with ball contact had the highest odds of hospital admission. CONCLUSIONS: Soccer accounted for a significant proportion of injuries presented to Canadian Hospitals Injury Reporting and Prevention Program emergency departments during 1994-2004. Further studies investigating potential interventional programs and techniques among this population are highly warranted.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".