Aetiology, epidemiology and treatment of musculoskeletal injuries in badminton players: a systematic review and meta-analysis
Bibliographic record
Abstract
The present study has two main goals: to conduct a systematic review of musculoskeletal injuries experienced by badminton players, and to examine the management of such injuries. Searches were conducted of the PROSPERO, PubMed, Scopus, and SPORTDiscus databases, from their inception until March 2023. The papers analysed were all based on a study population consisting of individuals aged 18 years or more, diagnosed with badminton-related injuries. The methodological quality assessments was using the Newcastle-Ottawa Scale and REVMAN. A total of 28 studies were included in the systematic review. In total, the analysis included 2435 participants. Of these athletes, 35.6% (1012) were female and 64.4% (1503) were male. By type of injury, sprains were the most commonly studied and the most prevalent, accounting for 36.06% of the sample. These were followed by muscle injuries, representing 23.86% of the total. Injuries to the joints were the least prevalent, accounting for 4.97% of the sample. Lower limb injuries accounted for 52.15% of the total. Of these, ankle injuries were the most common. Despite the generally low quality of the studies considered, the evidence suggests that musculoskeletal injuries, especially to the lower limb, most commonly affect badminton players of all levels.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.021 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".