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Record W2058329904 · doi:10.1002/mus.22072

Lower limb electromygraphy and kinematics of neuropathic diabetic patients during real‐life activities: Stair negotiation

2011· article· en· W2058329904 on OpenAlexaff
Andrea Naomi Onodera, Aline A. Gomes, Denise Pripas, Rinaldo A. Mezzarane, Isabel de Camargo Neves Sacco

Bibliographic record

VenueMuscle & Nerve · 2011
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversity of Victoria
FundersConselho Nacional de Desenvolvimento Científico e TecnológicoFundação de Amparo à Pesquisa do Estado de São Paulo
KeywordsElectromyographyPhysical medicine and rehabilitationMedicineAnkleForefootKinematicsPhysical therapySurgeryComplication

Abstract

fetched live from OpenAlex

INTRODUCTION: In this study we investigate the influence of diabetic neuropathy (DN) on lower limb electromyography (EMG) and kinematics during stair negotiation. METHODS: Forty-six adults (healthy and DN) performed stair ascent and descent tasks. Kinematic and EMG data were assessed unilaterally. RESULTS: DN patients had lower ankle dorsiflexion while ascending and plantarflexion while descending. This reduced dorsiflexion compromises proper ankle and knee positions necessary for an efficient lifting action by the vastus lateralis (VL). The mechanical disadvantage of VL at the beginning of the stance triggered prolonged VL activation at the end of stair ascent. In stair descent, DN patients showed lower tibialis anterior activity in the early phase that can potentially impair the mechanism of impact absorption when the forefoot contacts the step. CONCLUSIONS: Our results reveal an adaptive motor strategy in DN patients to overcome the challenge of stair ascent, which promoted more biomechanical deficits.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.165
Threshold uncertainty score0.624

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 head0.020
GPT teacher head0.273
Teacher spread0.253 · 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 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".

Quick stats

Citations25
Published2011
Admission routes1
Has abstractyes

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