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Record W4392351176 · doi:10.1136/bjsports-2024-ioc.12

778 FO12 – From scrum to sling: the shoulder injury landscape in 19 seasons of professional rugby union

2024· article· en· W4392351176 on OpenAlexaff
Eric Gibson, Stephen West, Sam Hudson, Matthew Cross, Aileen Taylor, John H M Brooks, Lindsay Starling, Simon Kemp, Keith Stokes

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicShoulder and Clavicle Injuries
Canadian institutionsAlberta Bone and Joint Health InstituteAlberta Children's Hospital
Fundersnot available
KeywordsMedicineAcromioclavicular jointPhysical therapyFootballInjury preventionPoison controlEmergency medicine

Abstract

fetched live from OpenAlex

<h3>Background</h3> Professional rugby union is a fast-paced collision sport with a high rate of injury. With the tackle often being a direct and forceful contact between opposing players at the shoulder level, it is a typical injury location. <h3>Objective</h3> To describe shoulder match and training injury rates, diagnosis, severity, and mechanisms in professional men’s rugby over 19 seasons. <h3>Design</h3> Prospective cohort study. <h3>Setting</h3> Professional Rugby Union (2002/2003 – 2021/2022). <h3>Participants</h3> Elite male athletes participating in a professional rugby league. Data were collected from all participating teams during each season of play (12/13 teams per season for 19 seasons). <h3>Interventions/Risk Factors</h3> Match and training exposure, as well as injuries were collected by club staff. <h3>Main Outcome Measures</h3> Shoulder-related injuries included any clavicular, scapular, acromioclavicular joint, rotator cuff, or shoulder joint, and surrounding tissue impairment. Any injury resulting in the athlete being unable to participate in rugby for &gt;24 hours after the day of injury were included. <h3>Results</h3> A match shoulder injury rate of 9.01 per 1000 match-hours (n=1330) was observed. The training shoulder injury rate was 0.15 per 1000 training hours (n=362). Shoulder injury diagnoses most frequently involved the acromioclavicular joint (n=595, 35%), dislocations and instability (n=432, 27%), and impingement/rotator cuff injury (318, 19%). The primary injury pathologies based on orchard code were ligament injuries (n=675, 39%), cartilage injuries (n=149, 9%), and muscle contusions (n=143, 8%). Time-loss due to shoulder injury was a median of 15 days (IQR 5–43 days). The mechanism of most shoulder injuries was tackling (n=592, 35%) or being tackled (n=419, 25%). <h3>Conclusions</h3> Shoulder injuries resulted in substantial time-loss from sport. The injury rates were similar to what has been previously reported. With such little change in match and training injury rates observed, novel training strategies and policies to prevent shoulder injury merits further investigation and innovation avenues.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.257
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.019
GPT teacher head0.395
Teacher spread0.376 · 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.

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

Citations0
Published2024
Admission routes1
Has abstractyes

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