Motor performance is not related to injury risk in growing elite-level male youth football players. A causal inference approach to injury risk assessment
Bibliographic record
Abstract
OBJECTIVE: To identify the causal relation between growth velocity and injury in elite-level youth football players, and to assess the mediating effects of motor performance in this causal pathway. DESIGN: Prospective cohort study. METHODS: We measured the body height of 378 male elite-level football players of the U13 to U15 age categories three to four months before and at the start of the competitive season. At the start of the season, players also performed a motor performance test battery, including motor coordination (Körperkoordinationstest für Kinder), muscular performance (standing broad jump, counter movement jump), flexibility (sit and reach), and endurance measures (YoYo intermittent recovery test). Injuries were continuously registered by the academies' medical staff during the first two months of the season. Based on the causal directed acyclic graph (DAG) that identified our assumptions about causal relations between growth velocity (standardized to cm/y), injuries, and motor performance, the causal effect of growth velocity on injury was obtained by conditioning on maturity offset. We determined the natural indirect effects of growth velocity on injury mediated through motor performance. RESULTS: In total, 105 players sustained an injury. Odds ratios (OR) showed a 15% increase in injury risk per centimetre/year of growth velocity (1.15, 95%CI: 1.05-1.26). There was no causal effect of growth on injury through the motor performance mediated pathways (all ORs were close to 1.0 with narrow 95%CIs). CONCLUSIONS: Growth velocity is causally related to injury risk in elite-level youth football players, but motor performance does not mediate this relation.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".