Risk Factors for Non-Contact Lower-Limb Injury: A Retrospective Survey in Pediatric-Age Athletes
Bibliographic record
Abstract
Background: Risk factors for non-contact lower-limb injury in pediatric-age athletes and the effects of lateral dominance in sport (laterally vs. non-laterally dominant sports) on injury have not been investigated. Purpose: To identify risk factors for non-contact lower-limb injury in pediatric-age athletes. Methods: Parents and/or legal guardians of 2269 athletes aged between 6–17 years were recruited. Each participant completed an online questionnaire that contained 10 questions about the athlete’s training and non-contact lower-limb injury in the preceding 12 months. Results: The multivariate logistic regression model determined that lateral dominance in sport (adjusted OR (laterally vs. non-laterally dominant sports), 1.38; 95% CI, 1.10–1.75; p = 0.006), leg preference (adjusted OR (right vs. left-leg preference), 0.71; 95% CI, 0.53–0.95; p = 0.023), increased age (adjusted OR, 1.21; 95% CI, 1.16–1.26; p = 0.000), training intensity (adjusted OR, 1.77; 95% CI, 1.43–2.19; p = 0.000), and training frequency (adjusted OR, 1.36; 95% CI, 1.25–1.48; p = 0.000) were significantly associated with non-contact lower-limb injury in pediatric-age athletes. Length of training (p = 0.396) and sex (p = 0.310) were not associated with a non-contact lower-limb injury. Conclusions: Specializing in laterally dominant sports, left-leg preference, increase in age, training intensity, and training frequency indicated an increased risk of non-contact lower-limb injury in pediatric-age athletes. Future research should take into account exposure time and previous injury.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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".