Neuromuscular Training Programs For Acl Tear Prevention In Young Female Soccer Athletes
Bibliographic record
Abstract
PURPOSE: To determine if neuromuscular training programs (NTP), compared to routine care, prevent the occurrence of an ACL tear in young female soccer athletes. METHODS: A Health technology assessment of the clinical and cost-effectiveness of neuromuscular training programs in an outpatient clinic setting consisting of clinics, schools, and soccer clubs. The target population patients were young female soccer athletes between the ages of 10 and 18. The intervention was a neuromuscular training programs with the control group as routine care. The main outcome measure analyzed was a symptomatic, full thickness, complete ACL tear. The timeframe of analysis was 1 year, or 2 full seasons. RESULTS: Inclusion criteria were randomized controlled trials, systematic reviews, meta-analyses, and economic analyses. 7 systematic reviews, 6 prospective cohorts, and 5 randomized controlled trials were identified for full-text review to assess the methodological quality. Only 1 randomized controlled trial, 1 meta-analysis and 1 economic analysis focused specifically on neuromuscular training versus routine care in preventing ACL tears in the adolescent female subgroup. The optimal age window was before the age of 18 with a 72 % risk reduction rate. A universal neuromuscular training program can lead to decreased ACL tears with costs as low as 1.25$ per player to as high as 25$ per player in a season. NTP is effective with a decreased incidence of injury from 3% to 1.1 % per season and could save 275$ per athlete in injury-related costs. CONCLUSION: Neuromuscular training programs are safe, efficacious in the real world, cost-effective, impact the quality of life of patients, and impact government budgets on a minimal basis.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".