High Sport Specialization Is Associated With More Musculoskeletal Injuries in Canadian High School Students
Bibliographic record
Abstract
OBJECTIVE: To describe levels of sport specialization in Canadian high school students and investigate whether sport specialization and/or sport participation volume is associated with the history of musculoskeletal injury and/or concussion. DESIGN: Cross-sectional study. SETTING: High schools, Alberta, Canada. PARTICIPANTS: High school students (14-19 years) participating in various sports. INDEPENDENT VARIABLES: Level of sport specialization (high, moderate, low) and sport participation volume (hours per week and months per year). MAIN OUTCOME MEASURES: Twelve-month injury history (musculoskeletal and concussion). RESULTS: Of the 1504 students who completed the survey, 31% were categorized as highly specialized (7.5% before the age of 12 years). Using multivariable, negative, binomial regression (adjusted for sex, age, total yearly training hours, and clustering by school), highly specialized students had a significantly higher musculoskeletal injury rate [incidence rate ratio (IRR) = 1.36, 95% confidence interval (CI), 1.07-1.73] but not lower extremity injury or concussion rate, compared with low specialization students. Participating in one sport for more than 8 months of the year significantly increased the musculoskeletal injury rate (IRR = 1.27, 95% CI, 1.02-1.58). Increased training hours significantly increased the musculoskeletal injury rate (IRR = 1.18, 95% CI, 1.13-1.25), lower extremity injury rate (IRR = 1.16, 95% CI, 1.09-1.24), and concussion rate (IRR = 1.31, 95% CI, 1.24-1.39). CONCLUSIONS: Approximately one-third of Canadian high school students playing sports were categorized as highly specialized. The musculoskeletal injury rate was higher for high sport specialization students compared with low sport specialization students. Musculoskeletal injuries and concussion were also more common in students who train more and spend greater than 8 months per year in one 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".