Injury incidence and characteristics in adolescent female footballplayers: A systematic review with meta-analysis of prospectivestudies
Bibliographic record
Abstract
To observe overall, training, and match injury incidence in female youth football. We also aimed to quantify the incidence of injuries by affected tissue and body location. The following databases were examined: PubMed, Web of Science, Scopus, SPORTDiscus, Cochrane and PEDro. Papers that reported overall injury incidence, training or match injury incidence were included. Additionally, studies had to be performed in adolescent female football players (13-19 years of age). The Newcastle-Ottawa Scale and the checklist of items that must be included in epidemiological football reports were used to assess methodological quality of the included articles. For the meta-analyses, a random effect model was used. A total of 13 studies were included. There were 2,333 injuries; incidence was higher during games (12.7/1000 h) compared to training sessions (2.3/1000 h). The injury match-to-training ratio was 5.8. The lower limbs were the region in which the greatest number of injuries occurred, with the ankle (1.2/1000 h) and knee (0.8/1000 h) having the most injuries. In relation to injured tissue, ligament injuries represented an incidence of 1.3/1000 h, followed by muscle injuries (0.9/1000 h). This study represents the first step towards the creation and implementation of preventative measures in female youth football. The results suggest that attention should be focused on ankle and knee injuries, since they are the most frequent and can lead to sport retirement in some cases depending on the severity.
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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 | 0.016 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.037 |
| Bibliometrics | 0.012 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".