Sociodemographic Predictors of Sport Injury in Adolescents
Bibliographic record
Abstract
OBJECTIVE: To examine sociodemographic risk factors for sport injury in adolescents. METHODS: This is a cross-sectional survey design in which a random sample of high school students (ages 14-19) completed an in-class survey (N = 2721). Students were asked questions regarding sociodemographic factors, sport participation, and sport injury in the past year. RESULTS: The incidence proportion of self-reported and medically treated sports injury, adjusting for the clustering effect of school, was 67.5 (95% CI; 64.2-71.1) and 43.2 (95% CI; 40.4-46.3) per 100 adolescents per year, respectively. Students from small towns had a lower risk of injury than those in the larger urban center (ORadjusted = 0.76, 95% CI 0.63-0.92). Non-Caucasian students had a lower risk of injury than did Caucasian students (ORadjusted = 0.63 (95% CI 0.5-0.79) for all sport injury and 0.57 (95% CI 0.47 - 0.7) for medically treated sport injury. Students with BMI in the 50th-90th percentiles had the greatest risk of sport injury. The risk of injury increased with weekly hours of participation. CONCLUSIONS: Location of residence, weekly exposure (participation hours), ethnicity, and BMI were simultaneous predictors of sport injuries in adolescents.
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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 | 0.001 | 0.003 |
| 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) | 0.002 | 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".