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Record W2039302622 · doi:10.1139/h08-058

An exploration of the association between frailty and muscle fatigue

2008· review· en· W2039302622 on OpenAlexaffvenue
Olga Theou, Gareth R. Jones, Tom J. Overend, Marita Kloseck, Anthony A. Vandervoort

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2008
Typereview
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsOkanagan University CollegeUniversity of British Columbia, Okanagan CampusUniversity of British ColumbiaWestern University
Fundersnot available
KeywordsSarcopeniaMuscle fatigueMedicineWeaknessMalnutritionMuscle weaknessPhysical medicine and rehabilitationDiseaseFrailty syndromeGerontologyInternal medicineElectromyography

Abstract

fetched live from OpenAlex

Frailty is increasingly recognized as a geriatric syndrome that shares common biomedical determinants with rapid muscle fatigue: aging, disease, inflammation, physical inactivity, malnutrition, hormone deficiencies, subjective fatigue, and changes in neuromuscular function and structure. In addition, there is an established relationship between muscle fatigue and core elements of the cycle of frailty as proposed by Fried and colleagues (sarcopenia, neuroendocrine dysregulation and immunologic dysfunction, muscle weakness, subjective fatigue, reduced physical activity, low gait speed, and weight loss). These relationships suggest that frailty and muscle fatigue are closely related and that low tolerance for muscular work may be an indicator of frailty phenotype.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.146
GPT teacher head0.387
Teacher spread0.241 · 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 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

Citations53
Published2008
Admission routes2
Has abstractyes

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