Prevention of Ankle Sprain Injuries in Youth Soccer and Basketball: Effectiveness of a Neuromuscular Training Program and Examining Risk Factors
Bibliographic record
Abstract
OBJECTIVES: The primary objective of this study was to examine the effectiveness of a neuromuscular training (NMT) warm-up program in reducing the risk of ankle sprain injury (ASI) in youth soccer and basketball. The secondary objective included the evaluation of risk factors for ASI. STUDY DESIGN: Secondary analysis of pooled data from 5 studies. PARTICIPANTS: Male and female youth (11-18 years) soccer and basketball players (n = 2265) in Alberta, Canada. OUTCOME MEASURES: Ankle sprain injury was the primary outcome and was recorded using a validated prospective injury surveillance system consistent in all studies. The primary exposure of interest was NMT warm-up, which included aerobic, strength, agility, and balance components. Multivariable Poisson regression, controlling for clustering by team and offset for exposure hours, was used to estimate incidence rate ratios (IRRs) with 95% confidence intervals (CIs), with considerations for confounding and effect modification and evaluating all covariates as potential risk factors. RESULTS: A total of 188 ASIs were reported in 171 players. Neuromuscular training significantly reduced the risk of ASI [IRR = 0.68 (95% CI; 0.46-0.99)]. Independent risk factors for ASI included previous ASI [IRR = 1.98 (95% CI; 1.38-2.81)] and participation in basketball versus soccer [IRR = 1.83 (95% CI; 1.18-2.85)]. Sex, age, body mass index, and previous lower extremity injury (without previous ASI) did not predict ASI (P > 0.05). CONCLUSIONS: Exposure to an NMT program is significantly protective for ASI in youth soccer and basketball. Risk of ASI in youth basketball is greater than soccer, and players with a history of ASI are at greater risk.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 |
| 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 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".