Injury Profile in Youth Female Athletes: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: An increasing number of epidemiological studies assessing the incidence, prevalence and severity of injury in youth female sport are available. However, no study has sought to synthesise the current evidence base across all youth female sport. As such, a systematic review and meta-analysis of injury in this cohort is necessary to understand the diversity of injury and its associated burden between sports in addition to identifying the density of research available. OBJECTIVE: To conduct a systematic review and meta-analysis of epidemiological data of injuries in youth female athletes with particular attention to injury incidence, mean days lost and injury burden. METHODS: Searches were performed in PubMed, EBSCO (SPORTDiscus with Full Text MEDLINE, APA PsycINFO, CINAHL, Academic Search Complete) and Cochrane databases. Studies were considered if they reported time-loss injury incidence or prevalence in youth female (≤ 19 years old) athletes. Study quality and risk of bias were assessed using STROBE-SIIS extension, Newcastle-Ottawa Scale, and funnel plots, respectively. Injury incidence and burden rate data were modelled using a mixed-effect Poisson regression model. Days lost data were modelled using a generalised linear mixed model. RESULTS: Thirty-two studies were included. The overall incidence rate, mean days lost per injury, and burden rate were 4.4 injuries per 1000 h (95% CI 3.3-5.9), 10 days (95% CI 6-15), and 46 days per 1000 h (95% CI 23-92), respectively. Forty percent of athletes sustained at least one time-loss injury. Competitive level was a significant moderator of match and training injury incidence, with elite youth athletes presenting greater pooled injury incidence estimates than non-elite athletes (p = 0.0315 and p = 0.0047, respectively). The influence of moderators on days lost and injury burden could not be determined due to an insufficient number of studies for analysis. CONCLUSION: Despite a broad inclusion criterion, there is limited injury surveillance research available across youth female sport. Outside of soccer, little research density is evidenced with single studies available in popular team sports such as Australian football and rugby union. Insufficient study numbers reporting mean days lost and injury burden data were available for analysis, and pooled days lost data could only be estimated for soccer. This highlights a need for future research to report days lost data alongside injury number and exposure so burden can be calculated and the full risk of injury to youth female athletes can be identified.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.018 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 teacher head, 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".