Injury Rates, Mechanisms, Risk Factors and Prevention Strategies in Youth Rugby Union: What’s All the Ruck-Us About? A Systematic Review and Meta-analysis
Notice bibliographique
Résumé
BACKGROUND: Rugby Union is a collision team sport played globally. Despite this, significant concerns have been raised regarding the sport's safety, particularly in youth players. Given this, a review of injury rates, risk factors and prevention strategies is required across different youth age groups as well as in males and females. OBJECTIVE: The objective of this systematic review (SR) and meta-analysis was to investigate injury and concussion rates, risk factors and primary prevention strategies in youth rugby. METHODS: To be included, studies were required to report either rates, risk factors or prevention strategies in youth rugby and to have a randomised controlled trial, quasi-experimental, cohort, case control, or ecological study design. Exclusion criteria included non-peer-reviewed grey literature, conference abstracts, case studies, previous systematic reviews and studies not written in English. Nine databases were searched. The full search strategy and list of sources are available and pre-registered on PROSPERO (Ref: CRD42020208343). Each study was assessed for risk of bias using the Downs and Black quality assessment tool. Meta-analyses were conducted using a DerSimonian Laird random effect model for each age group and sex. RESULTS: Sixty-nine studies were included in this SR. The match injury rates (using a 24-h time-loss definition) were 40.2/1000 match hours (95% CI 13.9-66.5) in males and 69.0/1000 match hours (95% CI 46.8-91.2) in females. Concussion rates were 6.2/1000 player-hours (95% CI 5.0-7.4) for males and 33.9/1000 player-hours (95% CI: 24.1-43.7) for females. The most common injury site was lower extremity (males) and the head/neck (females). The most common injury type was ligament sprain (males) and concussion (females). The tackle was the most common event associated with injury in matches (55% male, 71% females). Median time loss was 21 days for males and 17 days for females. Twenty-three risk factors were reported. The risk factors with the strongest evidence were higher levels of play and increasing age. Primary injury prevention strategies were the focus of only eight studies and included law changes (n = 2), equipment (n = 4), education (n = 1) and training (n = 1). The prevention strategy with the most promising evidence was neuromuscular training. The primary limitations included a broad range of injury definitions (n = 9) and rate denominators (n = 11) used, as well as a limited number of studies which could be included in the meta-analysis for females (n = 2). CONCLUSION: A focus on high-quality risk factor and primary prevention evaluation should be considered in future studies. Targeting primary prevention and stakeholder education remain key strategies in the prevention, recognition and management of injuries and concussions in youth rugby.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,021 | 0,053 |
| Méta-épidémiologie (sens strict) | 0,003 | 0,002 |
| Méta-épidémiologie (sens large) | 0,023 | 0,041 |
| Bibliométrie | 0,010 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,003 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».