Females Sustain more Ankle Injuries than Males in Youth Football
Bibliographic record
Abstract
This prospective study evaluated the incidence and pattern of acute injuries in youth (9- to 14-year- old) football players. Ten football clubs [n=730 players (567 males, 163 females)] participated in the 20-week follow-up study (January-June 2015). Data was collected by sending a standardized weekly SMS to players' parents/guardians with follow-up interviews for injured players. During the study period, 278 players (38%) sustained 410 acute injuries. The overall injury incidence for males and females was 6.47 (95% CI, 5.84-7.09) injuries per 1000 h of football exposure. Most injuries (40%) caused minimal absence from sports. Eighty-four percent of the injuries affected the lower extremities, with the ankle (30%), knee (17%), and thigh (16%) being the most commonly injured body sites. Females had significantly higher ankle injury rate (IRR) 1.85 (95% CI, 1.18-2.91, p=0.007) and non-contact ankle injury rate IRR 2.78 (95% CI, 1.91-4.02, p<0.001) than males. In conclusion, our results showed that the acute injury incidence among youth football is moderately high, and females are at higher risk for ankle injuries. Injury prevention programs aimed at preventing ankle injuries should be considered in the future.
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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 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".