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Enregistrement W2066922911 · doi:10.1177/2325967114s00056

An American Experience with a New Olympic Collision Sport: Rugby Sevens

2014· article· en· W2066922911 sur OpenAlexaff
Victor Lopez, Richard Ma, Meryle Weinstein, James L. Chen, Christopher M. Black, Arun Gupta, Erica D. Marcano, Answorth A. Allen

Notice bibliographique

RevueOrthopaedic Journal of Sports Medicine · 2014
Typearticle
Langueen
DomaineMedicine
ThématiqueSports injuries and prevention
Établissements canadiensAlberta Health Services
Organismes subventionnairesnon disponible
Mots-clésMedicinePopulationIncidence (geometry)Injury preventionPhysical therapyDemographyPoison controlMedical emergencyEnvironmental health

Résumé

récupéré en direct d'OpenAlex

Objectives: Rugby Sevens is a future Olympic collision sport that is played globally with a high incidence of injury. The sport is growing exponentially in the U.S. There is limited injury data on Rugby Sevens. Currently, there is no study evaluating incidence of injury in the National Rugby Sevens population in the U.S. The objective of this study was to characterize the injury rates among amateur Rugby Sevens players in the U.S. Methods: This was a prospective descriptive injury epidemiology study involving American Rugby Sevens tournaments from 2010-2013. The injury data were collected from both male and female Rugby Sevens players (13, 524 players) and conformed to the international consensus statement on rugby injury definitions. The study included a total of 1,127 teams competing in under-15 to national candidate level tournaments (USA Rugby Local Area, Territorial Union, National and All-Star Sevens Series, USA Sevens Invitational and Collegiate Rugby Championships). A total of 2746 matches were played, 2734 lasting 14-minutes (0.23 hours) in length and 12 lasting 20-minutes (0.33 hours) in length. The overall injury exposure for all players was 8858.9 playing hours (7 players per side * 2 teams per match * 0.23 hours per match * 2734 matches + 7 players per side * 2 teams per match * 0.33 hours per match * 12 matches). Player injury data were reported as proportion (%), mean (SD), and rate of injury as injuries per 1000 playing hours. Results: Incidence of combined medical attention and time-loss injuries was 97.7/1000 playing hours (n=875 injuries) (23.6±5 years old). Time-loss injuries alone were encountered at 43.1/1000 playing hours (n=380 injuries) (forwards, 14.3/1000, n=127; backs 25.7/1000, n=228) (RR: 1.8; 1.53-2.11, P < 0.001). Elite/national male competitors (242.6/1000) were injured more often than lower playing levels (147.6/1000) (P < 0.001). Most injuries were acute injuries (96%) that occurred during the tackling phase of play (97%), and it resulted mainly from contact with an opposing player (67%). The main injuries seen were ligament sprain in lower extremities (43%). Upper extremity injuries were seen more often among male players (76%) than female players (24%) (RR: 0.31, CI: 0.25-0.40, P < 0.001). Knee injuries were seen more often in women’s elite players than men’s elite players (P = 0.014). Head/neck injuries (backs, 58%; forwards, 42%) occurred more often in male players (74%) (RR: 0.34; CI: 0.26-0.44, P < 0.001). Conclusion: Injury prevention in American Rugby Sevens should focus on proper tackling technique as most injuries in our series occurred during tackling. We also saw a significant number of head/neck injuries in our U.S. playing population, which may reinforce the importance of proper tackling technique in this collision sport. The rate of head/neck injuries (23%) in our U.S. cohort (National candidates, 25%; lower competitors, 23%) was higher than the rate reported among international male Rugby Sevens players (5%) (Table 1). Overall, our National candidates had higher rates of time loss injuries than lower American amateur playing levels. Our observed injury rate among U.S. elite players is also higher than that reported for international male Rugby Sevens population. Understanding the injury profile of American Rugby Sevens is important to healthcare providers and would direct the growth and safety of this growing collision sport, allowing safe return-to-play decisions and formulation of prevention protocols.

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 enseignants

Ni 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.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,022
Score d'incertitude au seuil0,043

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,001
Bibliométrie0,0010,001
Études des sciences et des technologies0,0030,001
Communication savante0,0020,001
Science ouverte0,0000,004
Intégrité de la recherche0,0010,001
Charge utile insuffisante (le modèle a refusé de juger)0,0080,001

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.

Tête enseignante Opus0,008
Tête enseignante GPT0,280
Écart entre enseignants0,273 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations4
Publié2014
Routes d'admission1
Résumé présentoui

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