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Record W2091348560 · doi:10.1002/ajhb.20951

Variability of limb muscle size in young men

2009· article· en· W2091348560 on OpenAlexaff
Taku Wakahara, Kazumi Takeshita, Emika Kato, Masae Miyatani, Noriko I. Tanaka, Hiroaki Kanehisa, Yasuo Kawakami, Tetsuo Fukunaga

Bibliographic record

VenueAmerican Journal of Human Biology · 2009
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsToronto Rehabilitation InstituteUniversity of Toronto
Fundersnot available
KeywordsThighAnatomyPosterior compartment of thighMedicineLower limbAnterior compartment of thighUpper limbHumSurgery

Abstract

fetched live from OpenAlex

The purpose of this study was to determine the interindividual variability of the upper and lower limb muscle size in young men. Subjects were 655 Japanese men aged 18-39 years. They were sedentary and mildly to highly active individuals, including college athletes of various sports. Muscle thicknesses at each of the anterior and posterior portions of the upper arm, thigh, and lower leg were measured using B-mode ultrasonography. Interindividual variability of muscle thickness was evaluated by coefficients of variation (CVs). The CVs of muscle thicknesses were found to be in the order of upper arm posterior (17.7%), thigh anterior (14.8%), thigh posterior (12.6%), upper arm anterior (12.2%), lower leg anterior (9.8%), and lower leg posterior (9.4%). The CVs were significantly different between each pair of measurement sites except for those of upper arm anterior-thigh posterior and lower leg anterior-posterior. These differences remain significant even when the muscle thicknesses were normalized to the segment length. The observed differences in the size variability can be interpreted as muscle-related differences in hypertrophic responsiveness to resistance training. The muscle-dependent size variability may be related to the differences in the fiber-type composition and/or muscle usage in daily life among examined muscle groups.

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)0.001
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 score0.038
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.013
GPT teacher head0.308
Teacher spread0.295 · 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

Citations22
Published2009
Admission routes1
Has abstractyes

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