Epidemiology of injuries in male and female youth football players: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: To conduct a systematic review and meta-analysis of epidemiological data of injuries in male and female youth football players. METHODS: Searches were performed in MEDLINE/PubMed, Web of Science, Cochrane Library, and SPORTDiscus databases. Studies were considered if they reported injury incidence rate in male and female youth (≤19 years old) football players. Two reviewers (FJRP and ALV) extracted data and assessed trial quality using the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) statement and the Newcastle Ottawa Scale. The Grading of Recommendations Assessment, Development, and Evaluation approach determined the quality of evidence. Studies were combined using a Poisson random effects regression model. RESULTS: Forty-three studies were included. The overall incidence rate was 5.70 injuries/1000 h in males and 6.77 injuries/1000 h in females. Match injury incidence (14.43 injuries/1000 h in males and 14.97 injuries/1000 h in females) was significantly higher than training injury incidence (2.77 injuries/1000 h in males and 2.62 injuries/1000 h in females). The lower extremity had the highest incidence rate in both sexes. The most common type of injury was muscle/tendon for males and joint/ligament for females. Minimal injuries were the most common in both sexes. The incidence rate of injuries increased with advances in chronological age in males. Elite male players presented higher match injury incidence than sub-elite players. In females, there was a paucity of data for comparison across age groups and levels of play. CONCLUSION: The high injury incidence rates and sex differences identified for the most common location and type of injury reinforce the need for implementing different targeted injury-risk mitigation strategies in male and female youth football players.
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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 | 0.028 | 0.069 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.033 |
| Bibliometrics | 0.016 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".