Basketball vs. Hockey—The Changing Face of Sport-Related Injuries in Canada
Bibliographic record
Abstract
OBJECTIVE: To characterize and compare the incidence of basketball-related, soccer-related, and hockey-related injuries over a 10-year period. DESIGN: Cohort analysis of sport-related injuries using multiple Ontario healthcare databases. SETTING: Emergency department visits in Ontario, Canada. PATIENTS: Any patient who sustained musculoskeletal injuries sustained while playing basketball, soccer, or hockey between 2006 and 2017 were identified. ASSESSMENT OF RISK FACTORS: Sport of injury, age, sex, rurality index, marginalization status, and comorbidity score. MAIN OUTCOME MEASURES: Annual Incidence Density Rates of injury were calculated for each sport, and significance of trends was analyzed by assessing overlap of 95% confidence intervals. RESULTS: One lakhs eighty five thousand eighty hundred sixty-eight patients (median age: 16 years, interquartile range 13-26) received treatment for sport-related injuries (basketball = 55 468; soccer = 67 021; and hockey = 63 379). The incidence of basketball-related and soccer-related injuries increased from 3.4 (3.3-3.5) to 5.6 (5.5-5.7) and 4.4 (4.3-4.5) to 4.9 (4.8-5) per 10 000 person years, respectively, whereas the incidence of hockey-related injuries decreased from 4.7 (4.6-4.8) to 3.7 (3.6-3.8). Patients with basketball injuries were more marginalized (3.01 ± 0.74) compared with patients with soccer and hockey injuries (2.90 ± 0.75 and 2.72 ± 0.69, respectively). CONCLUSIONS: Accurate regional epidemiologic information regarding sports injuries can be used to guide policy development for municipal planning and sport program development. The trends and demographic patterns described highlight general and sport-specific injury patterns in Ontario. Populations with the highest incidence of injury, most notably adolescents and men older than 50, may represent an appropriate population for injury risk prevention.
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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 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.002 | 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".