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Record W2147758671 · doi:10.1093/gerona/60.2.148

Differences in Size, Strength, and Power of Upper and Lower Body Muscle Groups in Young and Older Men

2005· article· en· W2147758671 on OpenAlexafffund
Darren G. Candow, P D. Chilibeck

Bibliographic record

VenueThe Journals of Gerontology Series A · 2005
Typearticle
Languageen
FieldMedicine
TopicBody Composition Measurement Techniques
Canadian institutionsUniversity of Saskatchewan
FundersUniversity of Saskatchewan
KeywordsAnkleTorqueMuscle powerElbowMedicineElbow flexionMuscle strengthPhysical medicine and rehabilitationMuscle groupAnatomyPhysical therapyPhysics

Abstract

fetched live from OpenAlex

We compared muscle thickness, torque, normalized torque (torque/muscle thickness), and power at 1.05 rad/s and 3.14 rad/s in flexor and extensor muscles of the elbow and knee, and in ankle plantar flexors in young (n=22, 18-31 years) and older (n=28, 59-76 years) men. Young men had greater muscle thickness for all muscle groups (p<.01), except elbow extensors, which were similar to older men. Young men had greater torque and power at both velocities for all muscle groups (p<.01), and greater normalized torque at both velocities for the elbow extensors and knee flexors and at the fast velocity for knee extensors. Relative to young mean values, muscle thickness, and torque, normalized torque, and power in the older group were most affected for lower-body measurements, especially at the fast velocity. Torque, normalized torque, and power (especially at fast velocities), and muscle thickness in the lower body are affected more by aging than are upper body measures in men.

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)0.001
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.021
GPT teacher head0.286
Teacher spread0.264 · 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

Citations275
Published2005
Admission routes2
Has abstractyes

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Same venueThe Journals of Gerontology Series ASame topicBody Composition Measurement TechniquesFrench-language works237,207