Epidemiological characteristics of injury in 7–22-year-old badminton players by age and sex
Bibliographic record
Abstract
Badminton-related injury is thought to happen with increasing incidence among badminton players. Literature shown injury incidence across age is scarce. The objective was to investigate the epidemiological characteristics of badminton-related injuries among badminton players broken down by age and sex. This epidemiology study is a retrospective design in 7-22-year-old badminton players at a national competitive tournament with a questionnaire from 2018 to 2023. An injury was defined as somatic complaint with time loss and/or medical care. Badminton-related injuries were normalized to rate per 1000 training-hours calculated by Poisson distribution in the collected data according to age and gender. Among all the 711 badminton players, 60.3% (429 players) suffered from at least one badminton-related injury. Regardless of gender, the most frequently injured anatomical site was knee (male: 18.8%, female: 18.6%), followed by ankle (male: 13.4%, female: 13.4%) and lower back (male: 12.3%, female: 10.0%). In male badminton players, the shoulder (7.6%) ranked fourth as the plantar (6.7%) ranked fourth in female badminton players. The rate per 1000 training-hours of badminton-related injuries showed that male players peaked at age 15-16 years and female players peaked at age 17-18 years, with 3.24 injuries and 3.52 injuries per 1000 training-hours, respectively. In 7-22-year-old badminton players, knee, lower back, and shoulder injuries frequently occurred and were significantly associated with the incidence of badminton-related injuries. The peak incidence of badminton-related injuries was in 15-16-year-old male badminton players while the peak incidence was in 17-18-year-old female badminton players. These data have the potential to help target the most at-risk anatomical sites and the most at-risk badminton players precisely for injury prevention programs.
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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 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".