The Characteristics of Badminton-Related Pain in Pre-Adolescent and Adolescent Badminton Players
Bibliographic record
Abstract
Body pain, often considered as an early sign of injury in young players, warrants thorough study. This study aimed to examine the distribution of badminton-related pain and prevalence in pre-adolescent and adolescent badminton players. Profiles of badminton-related pain were surveyed using a questionnaire among 366 pre-adolescent and adolescent badminton players aged 7-12 years. The distribution of badminton-related pain was described, and the pain incidence was calculated. Proportions of pain per 1000-training-hour exposures were the main outcome measures. The analysis considered various age groups (7-8, 9-10, and 11-12 years) and years of badminton experience (≤2, 2-3, and > 3 years). In total, 554 cases of badminton-related pain were reported. The ankle was the most common site, followed by knee, plantar, shoulder, and lower back. The overall pain rate per 1000-training-hour exposure was 3.06. The 11-12-year-old group showed the highest pain rate, significantly greater than the 7-8-year-old group and the 9-10-year-old group. Additionally, the prevalence of pain exhibited an increasing trend with age. Finally, regardless of the age groups, participants with 2-3 years of badminton experience had the highest pain rate. These findings might help inform targeted interventions to reduce the high prevalence of pain in various body regions across pre-adolescent and adolescent badminton players.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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".