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Record W2612717655 · doi:10.3138/ptc.2016-16

Gluteus Medius and Minimus Muscle Structure, Strength, and Function in Healthy Adults: Brief Report

2017· article· en· W2612717655 on OpenAlexaffvenue
Lisa Whiler, Michael Fong, Seungjoo Kim, Anna Ly, Euson Yeung, Sunita Mathur

Bibliographic record

VenuePhysiotherapy Canada · 2017
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMediusIsometric exerciseMedicineGluteal musclesMuscle strengthAnatomyPhysical therapy

Abstract

fetched live from OpenAlex

Purpose: This article describes gluteus medius and minimus muscle thickness and hip abductor strength and function in healthy adults and explores the relationships between muscle thickness and function. Methods: Gluteus medius and minimus muscle thickness (B-mode ultrasound), isometric hip abductor strength (Biodex dynamometer), and lower extremity function (timed Trendelenburg test, Five-Times-Sit-to-Stand Test [FTSST], and lateral step-down test) were measured in healthy adults using a cross-sectional study design. Results: A total of 22 subjects were included: 10 men and 12 women, mean age 25.2 (SD 3.1) years, mean BMI 22.9 (SD 3.5) kilograms per metre squared. Muscle thickness of the gluteals was a mean 3.88 (SD 0.13) centimetres, and mean hip abductor peak torque was 111 (SD 43) newton-metres. FTSST mean time was 5.3 (SD 0.2) seconds, and median scores were 2.0 points for lateral step-down and 90 seconds for timed Trendelenburg. No significant relationships were found between gluteal muscle thickness and functional tests (rs=−0.28 to 0.37, ps=0.09–0.80) or strength (r=−0.24, p=0.28). Conclusion: Although hip abductors are key pelvic stabilizers for functional movements, gluteal muscle thickness was not associated with strength or function. This may be a result of agonist muscle activity, leading to an inability to isolate the gluteals, and to the ceiling effects of the functional tests.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.941
Threshold uncertainty score0.736

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 head0.222
Teacher spread0.216 · 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 designOther design
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

Citations20
Published2017
Admission routes2
Has abstractyes

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