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Record W2536234640

THE INFLUENCE OF INDIVIDUAL MUSCLE GROUPS ON FEMORAL STRAIN DISTRIBUTION DURING WALKING

2014· article· en· W2536234640 on OpenAlexvenueno aff
Manuel Zea, W. Brent Edwards

Bibliographic record

VenueJournal of undergraduate research in Alberta · 2014
Typearticle
Languageen
FieldMedicine
TopicHip disorders and treatments
Canadian institutionsnot available
Fundersnot available
KeywordsInverse dynamicsFemurFinite element methodContact forceKinematicsGround reaction forceStrain (injury)Materials scienceMechanicsAnatomyOrthodonticsStructural engineeringPhysicsMedicineSurgeryClassical mechanicsEngineering
DOInot available

Abstract

fetched live from OpenAlex

INTRODUCTION Mechanical strain resulting from muscle forces in locomotion plays an important role in the maintenance of bone health [1]. Depending on their magnitude and line of action, these muscle forces may lead to an overall increase or decrease in mechanical strain [2]. Thus, the exclusion of specific muscle groups in finite element models may have a significant impact of simulation results. Our purpose was to quantify the influence of individual muscle groups on the femoral strain distribution during walking using the finite element method. METHODS Kinematic and kinetic data were collected from ten males (age 24.9 ± 4.7 yrs; height 1.7 ± 0.1 m; mass 70.1 ± 8.9 kg) walking overground at 1.25 and 1.75 m/s. Joint reaction forces and moments were calculated using standard inverse dynamics procedures. Muscle and hip contact forces were quantified using musculoskeletal modeling [3] with a static optimization routine (cost function = sum of squared muscle stresses) [4]. For both walking speeds, two instances in stance were examined in the finite element models, coinciding with the first (Peak 1) and second peak (Peak 2) of the axial hip contact force. A finite element model of a young, healthy femur was generated from the VAHKUM database ( http://www.ulb.ac.be/project/vakhum/ ) and scaled to average subject size. Bone was assigned inhomogeneous linear-elastic material properties based on apparent density [5]. The femur was physiologically constrained at the lateral epicondyle, center of the patellar groove, and femoral contact point [6]. Muscle and hip contact forces were applied as point loads. Seven different simulations were run in ABAQUS Standard v6.1 (Providence, RI) for each of the two instances in stance at both 1.25 and 1.75 m/s. For baseline analyses, all the muscle loads were included. For subsequent analyses, muscles forces from specific muscle groups (Hip Adductors, Hip Abductors, Hip Flexors, Hip Extensors, Hip Internal Rotators, and Hip External Rotators) were orderly removed.  Principal strains were quantified along the anterior, lateral, medial, and posterior aspects of the femoral periosteal surface. RESULTS & DISCUSSIONS For all simulations, principal compressive and tensile strains were greatest on the medial and lateral aspect of the femur, respectively (Figure 1). Strain magnitudes for baseline analyses were consistent with in vivo measurements [7], ranging from 1,500-2,000 μe and increasing with walking speed. Strains were higher during Peak 1, compared to Peak 2, and removal of specific muscle groups had a greater influence on the strain distribution at this instance in stance. Removal of the hip extensors, abductors, and internal rotators resulted in an overall increase in the femoral strain distribution (Figure 1), suggesting these muscles have a prophylactic action to reduce femoral bending. On the other hand, removal of the hip flexors, adductors, and external rotators had a negligible effect of the femoral strain distribution (Figure 1), but these muscles were minimally activated during walking. CONCLUSIONS Specific muscle groups make important contributions to the femoral strain distribution during walking, and failure to include these muscles in finite element models will lead to erroneous conclusions regarding femoral strain magnitudes.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
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 head0.037
GPT teacher head0.348
Teacher spread0.311 · 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".

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Citations0
Published2014
Admission routes1
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