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Record W1984636183 · doi:10.3138/physio.61.4.197

Regional Muscle and Whole-Body Composition Factors Related to Mobility in Older Individuals: A Review

2009· review· en· W1984636183 on OpenAlexvenueno aff
Jason Kidde, Robin L. Marcus, Sheldon Smith, Paul C. LaStayo

Bibliographic record

VenuePhysiotherapy Canada · 2009
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsMuscle powerLean body massSarcopeniaComposition (language)MedicineMuscle strengthAnalysis of varianceMuscle massSkeletal muscleOdds ratioPhysical medicine and rehabilitationDemographyBody weightInternal medicine

Abstract

fetched live from OpenAlex

PURPOSE: To describe previously reported locomotor muscle and whole-body composition factors related to mobility in older individuals. METHODS: A narrative review of the literature, including a combination of search terms related to muscle and whole-body composition factors and to mobility in older individuals, was carried out. Statistical measures of association and risk were consolidated to summarize the common effects between studies. RESULTS: Fifty-three studies were reviewed. Muscle and whole-body factors accounted for a substantial amount of the variability in walking speed, with coefficients of determination ranging from 0.30 to 0.47. Muscle power consistently accounted for a greater percentage of the variance in mobility than did strength. Risks associated with high fat mass presented a minimum odds ratio (OR) of 0.70 and a maximum OR of 4.07, while the minimum and maximum ORs associated with low lean mass were 0.87 and 2.30 respectively. Whole-body and regional fat deposits accounted for significant amounts of the variance in mobility. CONCLUSION: Muscle power accounts for a greater amount of the variance in the level of mobility in older individuals than does muscle strength. Whole-body fat accounts for a greater amount of the variance in level of mobility than does whole-body lean tissue. Fat stored within muscle also appears to increase the risk of a mobility limitation in older individuals.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.840
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
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 head0.047
GPT teacher head0.397
Teacher spread0.350 · 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.

Study designNot applicable
Domainnot available
GenreReview

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

Citations34
Published2009
Admission routes1
Has abstractyes

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