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Record W1976492840 · doi:10.1177/0363546511427124

Profile of an American Amateur Rugby Union Sevens Series

2011· article· en· W1976492840 on OpenAlexaff
Victor Lopez, Gregory J. Galano, Christopher M. Black, Arun Gupta, Douglas E. James, Kristen Kelleher, Answorth A. Allen

Bibliographic record

VenueThe American Journal of Sports Medicine · 2011
Typearticle
Languageen
FieldMedicine
TopicSports injuries and prevention
Canadian institutionsAlberta Health Services
Fundersnot available
KeywordsMedicineConcussionAmateurPhysical therapyAthletesInjury preventionPoison controlEmergency medicineGeography

Abstract

fetched live from OpenAlex

BACKGROUND: Rugby union will enter the Olympic arena as Rugby Sevens in 2016. PURPOSE: To investigate the injury rate, injury type, and nature of injuries sustained in an amateur American rugby union sevens tournament series. STUDY DESIGN: Descriptive epidemiology study. METHODS: The rate, demographics, and characteristics of injury were evaluated in 1536 rugby union sevens players, from 128 sides, competing in 4 amateur 1-day tournaments in a USA Rugby local area rugby union. RESULTS: Forty-eight injuries occurred over 4 tournaments, for an injury rate of 55.4 injuries per 1000 playing hours. Head and neck injuries were most common (33.3% of injuries), followed by upper extremity (31.3%), trunk (18.8%), lower extremity (14.6%), and physiologic injuries (2.1%). The most common type of injury was ligament sprain (25.0%); followed by concussion (14.6%), hematoma/contusion (12.5%), muscle strain (10.4%), and abrasion (8.3%). Tackling was the most common mechanism of injury (74.5%). Males were injured at a significantly higher rate than females (RR, 7.5, P < .01), but no significant difference was observed based on player position (P = .08). CONCLUSION: Injuries are common among American amateur rugby athletes, with a substantial proportion involving the head and neck region. CLINICAL RELEVANCE: Understanding injury patterns in an American rugby union will be important for formulating future injury prevention, assessment, and treatment protocols.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.874
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.014
GPT teacher head0.276
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations72
Published2011
Admission routes1
Has abstractyes

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