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Record W4283731783 · doi:10.1055/a-1854-0052

Lean Mass, Muscle Strength, and Muscle Quality in Retired Rugby Players: The UK Rugby Health Project

2022· article· en· W4283731783 on OpenAlexaff
Ian Entwistle, Peter Francis, Matthew Lees, Patria Hume, Karen Hind

Bibliographic record

VenueInternational Journal of Sports Medicine · 2022
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsYork UniversityUniversity of Toronto
FundersAuckland University of Technology, New Zealand
KeywordsLean body massSarcopeniaMuscle massAthletesPhysical therapyAmateurGrip strengthMedicineFootballBody weightInternal medicine

Abstract

fetched live from OpenAlex

Abstract Although athletes from sports such as rugby have greater lean mass and strength during their playing careers, little is known about these characteristics post-retirement. Therefore, this study investigated lean mass, strength, and muscle quality in retired elite and amateur rugby players and non-contact athletes. Retired elite male rugby players (n=42, 43.9±10.3 y; 101.1±13.4 kg; 1.82±0.09 m), amateur rugby players (n=46, 48.0±10.5 y; 98.9±16.6 kg; 1.79±0.07 m) and non-contact athletes (n=30, 51.3±12.5 y; 91.3±13.4 kg; 1.79±0.07 m) received one total body dual-energy X-ray absorptiometry assessment of appendicular lean mass (ALM) and ALM index (ALMI). Grip strength was measured, and muscle quality (grip strength/unit of arm lean mass) was calculated. Sarcopenia was identified as ALMI<7.23 kg/m2 and handgrip strength<37.2 kg. Total lean mass, ALM and grip strength were greater in the elite rugby compared to amateur rugby and non-contact groups (p<0.01). There were no significant differences in muscle quality or sarcopenia prevalence. Retired elite rugby players had greater lean mass and grip strength than amateur rugby and non-contact athletes, although muscle quality was similar. The greater lean mass and strength might reflect genetic influences or previous participation in a highly physical sport.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.528
Threshold uncertainty score0.597

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
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 integrity0.0000.001
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 head0.049
GPT teacher head0.365
Teacher spread0.316 · 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

Citations4
Published2022
Admission routes1
Has abstractyes

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