Adiposity as a Risk Factor for Sport Injury in Youth: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: To determine whether high or low adiposity is associated with youth sport-related injury. DATA SOURCES: Ten electronic databases were searched to identify prospective studies examining the association between adiposity [body mass index (BMI) or body fat] and a future time-loss or medical attention sport-related musculoskeletal injury or concussion in youth aged 20 years and younger. Two independent raters assessed the quality (Downs and Black criteria) and risk of bias (Joanna Briggs Institute Critical Appraisal Tool). Random-effects meta-analyses were used to calculate pooled odds ratio [95% confidence interval (CI)] of injury. MAIN RESULTS: Of 11 424 potentially relevant records, 38 articles were included with 17 eligible for meta-analyses. In qualitative synthesis, no clear association was identified between adiposity and any sport injury; however, 16/22 studies identified high adiposity as a significant risk factor for lower-extremity injury. Meta-analyses revealed higher BMI in youth with any sport-related injury and lower BMI in youth who developed a bone stress injury (BSI) compared with noninjured controls. The pooled OR (95% CI) examining the association of BMI and injury risk (excluding bone injury) was 1.18 (95% CI: 1.03-1.34). A major source of bias in included articles was inconsistent adjustment for age, sex, and physical activity participation. CONCLUSIONS: Level 2b evidence suggests that high BMI is associated with greater risk of youth sport injury, particularly lower-extremity injury and excluding BSI or fracture. Although pooled mean differences were low, anthropometric risk of injury seems to be dependent on type and site of injury in youth sport.
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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 | 0.007 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.010 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".