Preventing lower extremity sport injury through a high intensity neuromuscular training program in a junior high school setting
Bibliographic record
Abstract
Background Neuromuscular training programs have shown to decrease injury rates in sport specific domains; however, there is limited research in non-sport populations. Objective Examine the effectiveness of reducing sport injury through a high intensity, neuromuscular training program aimed at school youth. Design Pilot, randomised controlled trial. Participants were randomised by school, to either intervention or control programs in year one. Year two, the intervention program was implemented in the control school. Setting Physical education (PE) classes. Participants 725 (year one) and 314 (year two) consenting participants aged 11–15. Intervention Intervention group completed a 15 min, high intensity, neuromuscular training warm-up, three times per week for 12 weeks. The control group warm-up was the same duration, with standard of care components. Main outcome measurements Sport injury, defined as any sport injury that required medical attention, and/or cessation of the activity, and/or time loss from sport. Secondary outcomes; aerobic fitness (VO2maximum (ml/kg/min), vertical jump (cm) and balance (s). Results Incidence rate ratios (IRR), adjusted for clustering by class, previous injury and exposure hours, estimated the intervention program was protective of all sport injury (IRR=0.29 (95% CI 0.18 to 0.46)), lower extremity injury (IRR=0.30 (95% CI 0.17 to 0.53)), and time loss injury (IRR=0.43 (95% CI 0.20 to 0.94)). The intervention group showed greater improvement in aerobic fitness (2.14 ml/kg/min (95% CI 1.17 to 2.59)) (t=-3.86, p=0.0001) and vertical jump (4.16 cm (95% CI 3.65 to 4.66)) (t=-3.67, p=0.0003) compared to the control group. No statistical differences were detected in balance measures. Conclusions A neuromuscular training program implemented in PE classes was protective of all sport injury, lower extremity injury and time loss injury. Results pertaining to year two will be determined and presented.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".