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Record W1964649111 · doi:10.1139/h05-014

Chronic exercise and skeletal muscle power in older men

2006· article· en· W1964649111 on OpenAlexvenueno aff
Hans C. Dreyer, E. Todd Schroeder, Steven A. Hawkins, T. J. Marcell, K. M. Tarpenning, Alberto F. Vallejo, Nicole E. Jensky, Gabriel Q. Shaibi, Stefany Spears, Ryan Yamada, R. A. Wiswell

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2006
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsnot available
FundersFoundation for Physical Therapy
KeywordsAbsolute powerSkeletal muscleResistance trainingMedicineMuscle strengthMuscle powerLeg pressMuscle massSarcopeniaLeg musclePhysical therapyInternal medicineOne-repetition maximumPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

We sought to determine the effects of age and chronic exercise on muscle power in older males. We examined 32 older males 60-74 years of age and grouped as sedentary (CON, n = 11), chronic endurance trained (ET, n = 10), and chronic endurance trained + resistance training (ET + RT, n = 11). Exercise history was obtained by questionnaire. Absolute strength and power measures were obtained by the one-repetition maximum method. Relative strength and power were determined by dividing the absolute measure by the muscle mass involved in the exercise. Total and regional muscle mass was measured by DXA. Absolute and relative leg power were not significantly different among the 3 groups. In contrast, absolute leg press strength was greater in ET + RT compared with CON, and relative leg press strength was greater in ET and ET + RT compared with CON. Chronic running combined with resistance training may therefore enhance absolute and relative muscle strength in older adults, but does not influence muscle power. Endurance exercise may inhibit the ability of resistance exercise to positively influence skeletal muscle power.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.945
Threshold uncertainty score0.423

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)0.0000.000

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 head0.006
GPT teacher head0.232
Teacher spread0.226 · 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

Citations14
Published2006
Admission routes1
Has abstractyes

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