Players with high physical fitness are at greater risk of injury in youth football
Bibliographic record
Abstract
The aim of this study was to investigate physical fitness, football-specific skills, and their association with injury risk in youth football. Altogether 447 male and female players aged 9-14 years (median 12 years) participated in performance tests and prospective follow-up. The physical fitness tests included five-jump test for distance, 30-m sprint, football-specific figure of eight agility, countermovement jump, and Yo-Yo intermittent endurance test level 1. The football-specific skill tests included dribbling and passing tests. Injuries and exposure were registered during the 20-week follow-up. Our candidate risk factors were low/high level of physical fitness measured with a composite score of physical fitness tests and low/high level of football-specific skills measured with a composite score of dribbling and passing tests. Secondarily, we investigated performance in individual tests and their association with injury risk. During the follow-up, players reported 565 injuries (264 acute and 301 overuse injuries). High level of physical fitness was associated with increased rate of all injuries (age-, sex-, and mean team exposure-adjusted IRR: 1.28, 95% CI: 1.04-1.58). The level of football-specific skills had no influence on the overall injury rate. Burden of overuse injuries, but not acute injuries was significantly higher in most fit players compared with the players in the reference group (IRR: 2.09, 95% CI: 1.04-4.24). In conclusion, most fit players were at greater risk of sustaining injuries in youth competitive football.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 | 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".