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Record W2064250643 · doi:10.1002/mus.22051

Electromyography and kinematic changes of gait cycle at different cadences in diabetic neuropathic individuals

2011· article· en· W2064250643 on OpenAlexaff
Aline A. Gomes, Andrea Naomi Onodera, Mitie E.I. Otuzi, Denise Pripas, Rinaldo A. Mezzarane, Isabel de Camargo Neves Sacco

Bibliographic record

VenueMuscle & Nerve · 2011
Typearticle
Languageen
FieldMedicine
TopicDiabetic Foot Ulcer Assessment and Management
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCadencePhysical medicine and rehabilitationAnkleElectromyographyGaitKinematicsGait cycleRange of motionPhysical therapyMedicineAnatomyPhysics

Abstract

fetched live from OpenAlex

INTRODUCTION: Changes in gait cadence caused by challenging situations in daily life might induce higher demand for strength and propulsion in diabetic neuropathic (DN) subjects. METHODS: Forty-six subjects (healthy and DN) walked at two cadences (self-selected and 25% higher). Kinematic and electromyographic data were obtained from lower limbs and compared across the gait cycle. RESULTS: DN subjects showed a delayed peak in plantarflexor activity along the whole cycle (irrespective of cadence) compared with healthy subjects. However, during the imposed cadence, DN individuals showed reduced ankle range of motion along the entire cycle compared with the self-selected condition and healthy individuals walking at both cadences (P = 0.002). CONCLUSIONS: These findings suggest that when diabetic individuals face a new challenging situation that induces a higher demand for muscle strength and propulsion, the necessary range of motion and neuromuscular control around distal joints are insufficient.

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)0.001
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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".

Quick stats

Citations88
Published2011
Admission routes1
Has abstractyes

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