Who Does Not Respond to Injury Prevention Warm-up Programs? A Secondary Analysis of Trial Data From Neuromuscular Training Programs in Youth Basketball, Soccer, and Physical Education
Bibliographic record
Abstract
OBJECTIVES: To identify factors associated with nonresponse to neuromuscular training (NMT) warm-up programs among youth exposed to NMT warm-ups. METHODS: This is a secondary analysis of youth (aged 11–18 years) in the intervention groups of 4 randomized controlled trials in high school basketball, youth community soccer, and junior high school physical education. Youth who were exposed to NMT and who sustained an injury during the study were considered nonresponders. Odds ratios (ORs) were based on generalized estimating equations logistic regression controlling for clustering by team/class and adjusted for age, weight, height, balance performance, injury history, sex, and sport (soccer/basketball/physical education). RESULTS: A total of 1793 youth were included. Youth with a history of injury in the previous year had higher odds (OR = 1.64; 95% CI: 1.14, 2.37) of injury during the study, and females were more likely (OR = 1.67; 95% CI: 1.21, 2.31) to sustain an injury than males who were participating in NMT. Age was not associated with the odds of sustaining an injury (OR = 1.10; 95% CI: 0.93, 1.30). Soccer players benefited most from greater adherence, with 81% lower odds of injury (OR = 0.19; 95% CI: 0.06, 0.57) when completing 3 NMT sessions a week compared with 1 session per week. CONCLUSION: Factors associated with nonresponse to an NMT warm-up program were female sex, history of injury during the previous 12 months, and lower weekly NMT session adherence in some sports (soccer). J Orthop Sports Phys Ther 2023;53(2):94–102. Epub: 9 December 2022. doi:10.2519/jospt.2022.11526
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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 | 0.016 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| 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 | 0.001 | 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".