Evaluation of Risk Factors for Injury in Adolescent Soccer
Bibliographic record
Abstract
BACKGROUND: There are limited data on the epidemiology of adolescent soccer injury across all levels of play. HYPOTHESIS: Through implementation and validation of an injury surveillance system in adolescent soccer, risk factors for injury will be identified. STUDY DESIGN: Descriptive epidemiology study. METHODS: The study population was a random sample of 21 adolescent soccer teams (ages 12-18). A certified athletic therapist completed preseason baseline measurements and did weekly assessments of any identified soccer injury. The injury definition included any injury occurring in soccer that resulted in 1 or more of the following: medical attention, the inability to complete a session, or missing a subsequent session. RESULTS: Based on completeness of data in addition to validity of time loss, this method of surveillance has proven to be effective. The overall injury rate during the regular season was 5.59 injuries per 1,000 player hours (95% confidence interval, 4.42-6.97). Soccer injury resulted in time loss from soccer for 86.9% of the injured players. Ankle and knee injuries were the most common injuries reported. Direct contact was reported to be involved in 46.2% of all injuries. There was an increased risk of injury associated with games versus practices (relative risk = 2.89; 95% confidence interval, 1.69-5.21). The risk of injury in the under 14 age group was greatest in the most elite division. Having had a previous injury in the past 1 year increased the risk of injury (relative risk = 1.74; 95% confidence interval, 1.0-3.1). CONCLUSION: There were significant differences in injury rates found by division, previous injury, and session type (practice vs game). Future research should include the use of such a surveillance system to examine prevention strategies for injury in adolescent soccer.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".