Adherence to an Injury Prevention Warm-Up Program in Children’s Soccer—A Secondary Analysis of a Randomized Controlled Trial
Bibliographic record
Abstract
This study examined the impact of high adherence to a neuromuscular training (NMT) warm-up on the risk of lower extremity (LE) injuries in children’s soccer. Twenty U11–U14 youth clubs (n = 92 teams, 1409 players) were randomized into intervention (n = 44 teams) and control (n = 48 teams) groups. The intervention group was advised to perform an NMT warm-up 2 to 3 times a week for 20 weeks. Team adherence, injuries, and exposure were registered throughout the follow-up. Primary outcomes were the incidence of soccer-related acute LE injuries and the prevalence of overuse LE injuries. Intervention teams conducted mean 1.7 (SD 1.0) NMT warm-ups weekly through follow-up. The seasonal trend for adherence declined significantly by −1.9% (95% CI −0.8% to −3.1%) a week. There was no difference in the incidence of acute injuries nor the prevalence of overuse LE injuries in high team adherence group (n = 17 teams) compared to controls. However, the risk for acute noncontact LE injuries was 31% lower in the high team adherence group compared to controls (IRR 0.69, 95% CI 0.49 to 0.97). In an efficacy analysis (n = 7 teams), there was a significant reduction of 47% in the rate of noncontact LE injuries (IRR 0.53, 95% CI 0.29 to 0.97). In conclusion, teams conducted NMT warm-up sessions regularly, but with a declining trend. A greater protective effect was seen in teams with the highest adherence to the NMT warm-up.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".