Neuromuscular training injury prevention strategies in youth sport: a systematic review and meta-analysis
Bibliographic record
Abstract
Youth have very high participation and injury rates in sport. Sport is the leading cause of injury in youth. Sport injury reduces future participation in physical activity which adversely affects future health. Sport injury may lead to overweight/obesity and post-traumatic osteoarthritis. The objective of the systematic review and meta-analysis was to evaluate the efficacy of injury prevention neuromuscular training strategies in youth sport. Three electronic databases were systematically searched up to September 2014. Studies selected met the following criteria: original data; analytic prospective design; investigated a neuromuscular training prevention strategy intervention(s) and included outcomes for injury sustained during sport participation. Two authors assessed the quality of evidence using Downs and Black (DB) criteria. Meta-analyses including randomised controlled trials only (RCTs) to ensure study design homogeneity were completed for lower extremity and knee injury outcomes. Of 2504 potentially relevant studies, 25 were included. Meta-analysis revealed a combined preventative effect of neuromuscular training in reducing the risk of lower extremity injury (incidence rate ratio: IRR=0.64 (95% CI 0.49 to 0.84)). Though not statistically significant, the point estimate suggests a protective effect of such programmes in reducing the risk of knee injury (IRR=0.74 (95% CI 0.51 to 1.07)). There is evidence for the effectiveness of neuromuscular training strategies in the reduction of injury in numerous team sports. Lack of uptake and ongoing maintenance of such programmes is an ongoing concern. A focus on implementation is critical to influence knowledge, behaviour change and sustainability of evidence informed injury prevention practice.
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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 | 0.011 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.034 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".