A School-Based Injury Prevention Program to Reduce Sport Injury Risk and Improve Healthy Outcomes in Youth
Bibliographic record
Abstract
OBJECTIVE: To examine a school-based high-intensity neuromuscular training (NMT) program in reducing sport injury risk and improving fitness in youth. PARTICIPANTS: Students (ages 11-15) (n = 725) in physical education (PE) classes were randomized by school to intervention or control groups. INTERVENTION: A 12-week high-intensity NMT program (including aerobic, strength, balance, and agility components) was designed to reduce sport injury risk and improve measures of fitness. The control program was a standard of practice warm-up (including running and stretching). RESULTS: A Poisson regression model using an intent-to-treat analysis demonstrated a reduced risk of sport injury: incidence rate ratio (IRR)all injury = 0.30 (95% CI, 0.19-0.49), IRRlower extremity injury = 0.31 (95% CI, 0.19-0.51), IRRankle sprain injury = 0.27 (95% CI, 0.15-0.50), and IRRknee sprain injury = 0.36 (95% CI, 0.13-0.98). A change in waist circumference: -0.99 centimeters (95% CI, -1.84 to -0.14) and an increase in indirect measures of aerobic fitness: 1.28 mL·kg·min (95% CI, 0.66-1.90) in the intervention school compared with the control school also occurred. CONCLUSIONS: A NMT program in junior high school PE class was efficacious in reducing sport-related injury and improving measures of adiposity and fitness in the intervention group.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".