Risk factors for groin injury in sport: an updated systematic review
Bibliographic record
Abstract
BACKGROUND: The identification of risk factors for groin injury in sport is important to develop and implement injury prevention strategies. OBJECTIVE: To identify and evaluate the evidence examining risk factors for groin injury in sport. MATERIAL AND METHODS: Nine electronic databases were systematically searched to June 2014. Studies selected met the following criteria: original data; analytic design; investigated a risk factor(s); included outcomes for groin injury sustained during sport participation. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were followed and two independent authors assessed the quality and level of evidence with the Downs and Black (DB) criteria and Oxford Centre of Evidence-Based Medicine model, respectively. RESULTS: Of 2521 potentially relevant studies, 29 were included and scored. Heterogeneity in methodology and injury definition precluded meta-analyses. The most common risk factors investigated included age, hip range of motion, hip adductor strength and height. The median DB score across studies was 11/33 (range 6-20). The majority of studies represented level 2 evidence (cohort studies) however few considered the inter-relationships between risk factors. There is level 1 and 2 evidence that previous groin injury, higher-level of play, reduced hip adductor (absolute and relative to the hip abductors) strength and lower levels of sport-specific training are associated with increased risk of groin injury in sport. CONCLUSIONS: We recommended that investigators focus on developing and evaluating preparticipation screening and groin injury prevention programmes through high-quality randomised controlled trials targeting athletes at greater risk of injury.
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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 | 0.008 | 0.029 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.011 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".