Risk factors for groin injury in sport: an updated systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 it