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Record W2099381894 · doi:10.1002/mus.10386

Muscle fiber number in the biceps brachii muscle of young and old men

2003· article· en· W2099381894 on OpenAlexaff
Cliff S. Klein, Greg D. Marsh, Robert J. Petrella, Charles L. Rice

Bibliographic record

VenueMuscle & Nerve · 2003
Typearticle
Languageen
FieldEngineering
TopicMuscle activation and electromyography studies
Canadian institutionsSt Joseph's Health CentreLawson Health Research InstituteWestern University
Fundersnot available
KeywordsBicepsAnatomyMuscle biopsyAtrophyMedicineMuscle fibreMuscle contractionFiber typeFiberElbowInternal medicineSkeletal muscleBiopsyChemistry

Abstract

fetched live from OpenAlex

We have compared the number of muscle fibers in the biceps brachii muscle (BB) of six old men (82.3 +/- 4.3 years) and six young men (21.2 +/- 1.9 years). Muscle fiber number was estimated by dividing the maximal area of the BB, determined with magnetic resonance imaging, by the mean fiber area of the BB determined in a muscle biopsy. The percentage of type II fibers in the BB ( approximately 60%) and the type I fiber area were not different between the groups. The BB area (-26%), type II fiber area (-24%), mean fiber area (-20%), and maximal voluntary contraction strength (MVC) of the elbow flexor muscles (-27%) were lower in the old than young group. However, the estimated number of muscle fibers was not significantly different between the young (253000) and old (234000) men. Consequently, the smaller BB area of the old men could be explained primarily by a smaller type II fiber size. These findings suggest that old age is not associated with a reduced number of muscle fibers in the BB. The relative contribution of a reduction in fiber number to age-related muscle atrophy may be muscle-dependent.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.010
GPT teacher head0.214
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), 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

Citations147
Published2003
Admission routes1
Has abstractyes

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