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Record W2613433810 · doi:10.5430/jbei.v3n2p36

Biomechanical analysis to characterize the impact of knee osteoarthritis on hip, knee, and ankle kinematics

2017· article· en· W2613433810 on OpenAlexafffund
Neila Mezghani, Delphine Billard, Youssef Ouakrim, Alexandre Fuentes, Nicola Hagemeister, Jacques A. de Guise

Bibliographic record

VenueJournal of Biomedical Engineering and Informatics · 2017
Typearticle
Languageen
FieldMedicine
TopicOsteoarthritis Treatment and Mechanisms
Canadian institutionsÉcole de Technologie SupérieureUniversité TÉLUQ
FundersNatural Sciences and Engineering Research Council of CanadaMitacsFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsAnkleOsteoarthritisGaitKinematicsMedicinePhysical medicine and rehabilitationBiomechanicsKnee JointTreadmillGait analysisPhysical therapySurgeryAnatomyPathology

Abstract

fetched live from OpenAlex

Background: Numerous studies use a biomechanical assessment to evaluate joint function in knee pathologies such as osteoarthritis. However, most of them focus only on the knee and the consequences of the pathology on other lower limb joints are poorly documented. The objective of this study is to analyze the impact of knee osteoarthritis on ipsilateral hip and ankle joint during gait.Methods: Three-dimension (3D) angular kinematic patterns of the three joints were analyzed on 32 patients diagnosed with knee osteoarthritis (OA) and a control group of 15 asymptomatic subjects (AS). Kinematic data was captured during treadmill gait trials at a self-selected comfortable speed. Analysis of covariance (ANCOVA) was performed on selected points of interest from 3D kinematic patterns of the hip, knee and ankle joints to compare both groups. The significance level was set at p = .05.Results: Gait 3D kinematic gait patterns of OA patients revealed significant differences with those of AS subjects at the three joints.Conclusions: Results suggest that patients with knee osteoarthritis also present alterations in hip and ankle kinematic during gait that should be considered when tailoring conservative treatments.

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 score0.903
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.011
GPT teacher head0.260
Teacher spread0.249 · 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

Citations6
Published2017
Admission routes2
Has abstractyes

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