Risk Factors For Injury An Adolescent Soccer
Bibliographic record
Abstract
PURPOSE To implement and validate an injury surveillance program and examine risk factors for injury in adolescent soccer. METHODS This is a prospective cohort study. The study population was a random sample of 21 soccer teams from a Calgary Minor Soccer Club (n = 317 players). One team was randomly selected to participate from each skill division, from each of U18, U16, and U14 age groups for both boys and girls. A Certified Athletic Therapist completed pre-season baseline evaluations and assessed any identified injury occurring in soccer on a weekly basis. The injury definition included any injury occurring during the regular soccer season that required medical attention and/or removal from a session and/or missing a subsequent session. RESULTS Based on completeness of data for both injury and exposure information in addition to validity of diagnosis and time-loss, this method of surveillance has proven to be effective. The overall injury rate (IR) during regular season was 4.37 injuries/1000 player hours (95% CI; 3.34–5.61). The mechanism of injury was identified as direct contact with another player or equipment in 44.9% of all injuries reported. Soccer injury resulted in time loss of at least one soccer session in 86.9% of the players. Ankle and knee injuries were the most common injuries by body region in both boys and girls. It appears that girls may be at greater risk of knee ligament injuries than boys (RR = 2.53 [95% CI; 0.73–11.05]). The risk of injury in U14 and U16 age groups was greatest in the most elite division. There was an increased risk of injury in players who reported an injury in the previous one year (RR = 1.74 [95% CI; 1.0–3.1]). Players who are left leg dominant may be at greater risk of injury compared to those who are right leg dominant (RR=2.06 [0.84–4.37]). There was no apparent increased risk of injury associated with baseline measures of lower extremity flexibility, dynamic balance ability, endurance or lower extremity functional strength at baseline. CONCLUSION There were significant differences in injury rates found by division, previous injury history, and session type (practice vs. game). Future research includes the use of such a surveillance system to examine prevention strategies for injury in minor 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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".