Examining Measures of Weight as Risk Factors for Sport-Related Injury in Adolescents
Bibliographic record
Abstract
Objectives. To examine body mass index (BMI) and waist circumference (WC) as risk factors for sport injury in adolescents. Design. A secondary analysis of prospectively collected data from a pilot cluster randomized controlled trial. Methods. Adolescents () at the ages of 11–15 years from two Calgary junior high schools were included. BMI (kg/m2) and WC (cm) were measured from direct measures at baseline assessment. Categories (overweight/obese) were created using validated international (BMI) and national (WC) cut-off points. A Poisson regression analysis controlling for relevant covariates (sex, previous injury, sport participation, intervention group, and aerobic fitness level) estimated the risk of sport injury [incidence rate ratios (IRR) with 95% confidence intervals (CI)]. Results. There was an increased risk of time loss injury (IRR = 2.82, 95% CI: 1.01–8.04) and knee injury (IRR = 2.07, 95% CI: 1.00–6.94) in adolescents that were overweight/obese; however, increases in injury risk for all injury and lower extremity injury were not statistically significant. Estimates suggested a greater risk of time loss injury [IRR = 1.63 (95% CI: 0.93–2.47)] in adolescents with high measures of WC. Conclusions. There is an increased risk of time loss injury and knee injury in overweight/obese adolescents. Sport injury prevention training programs should include strategies that target all known risk factors for 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".