Sports injuries during Kabaddi: a literature review
Bibliographic record
Abstract
Background and Objective: Kabaddi is an athletic contact sport that involves power, aggression, fine mind-body balance, agility, and swift reflexes. Since Kabaddi is a contact sport, athletes face a higher risk of injuries during competition and training. There is a paucity of literature regarding sports injuries during this game. This review article lays down a plinth-stone for global recognition of this game and associated sports injuries, intending to improve awareness in the sports medicine fraternity and boost further research related to this topic. Methods: A comprehensive synthesis of the relevant literature was conducted to provide insights for clinicians, trainers, and players to enhance injury prevention and rehabilitation strategies. Key Content and Findings: Kabaddi, a traditional Indian sport, is gaining popularity worldwide. Its physically demanding nature poses a significant risk for sports injuries among players. Anterior cruciate ligament (ACL) ruptures with concurrent meniscal injuries are the most common soft tissue injuries in Kabaddi. However, due to the sport's aggressive and physical nature, nearly all types of injuries are possible. Conclusions: This review article highlights the mechanisms of injury and common injuries associated with Kabaddi. It also offers a comprehensive overview of the sport's origin, regulations, and etiquette. This review concludes that a multidisciplinary approach involving physiotherapists, sports orthopaedic specialists, and medical teams is crucial for conditioning players and minimizing injury risk.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".