A four year prospective study of injuries in elite Ontario youth provincial and national soccer players during training and matchplay.
Bibliographic record
Abstract
INTRODUCTION: With over 200 million amateur players worldwide, soccer is one of the most popular and internationally recognized sports today. By understanding how and why soccer injuries occur we hope to reduce prevalent injuries amongst elite soccer athletes. METHODS: Via a prospective cohort, we examined both male and female soccer players eligible to train with the Ontario Soccer Association provincial program between the ages of 13 to 17 during the period of October 10, 2008 and April 20, 2012. Data collection occurred during all player exposures to potential injury. Exposures occurred at the Soccer Centre, Ontario Training grounds and various other venues on multiple playing surfaces. RESULTS: A total number of 733 injuries were recorded. Muscle strain, pull or tightness was responsible for 45.6% of all injuries and ranked as the most prevalent injury. DISCUSSION: As anticipated, the highest injury reported was muscular strain, which warrants more suitable preventive programs aimed at strengthening and properly warming up the players' muscles.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".