MétaCan
Menu
Back to cohort
Record W76093337

Gait analysis using a force-measuring gangway: intrasession repeatability in healthy adults.

2011· article· en· W76093337 on OpenAlexaff
Louis‐Nicolas Veilleux, Maxime T. Robert, Laurent Ballaz, Martin Lemay, Frank Rauch

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldEngineering
TopicLower Extremity Biomechanics and Pathologies
Canadian institutionsCentre Hospitalier Universitaire Sainte-Justine
Fundersnot available
KeywordsBarefootGround reaction forceIntraclass correlationGaitRepeatabilityForce platformPhysical medicine and rehabilitationGait analysisPreferred walking speedPhysical therapyMedicineMathematicsReproducibilityKinematicsStatisticsPhysics
DOInot available

Abstract

fetched live from OpenAlex

OBJECTIVES: The goal of the present study was to determine the repeatability of gait parameters measured by a force plate gait analysis system (Leonardo Mechanograph® GW). METHODS: Fifteen healthy adult participants walked at a self-selected speed on a 10 m long walkway. Vertical ground reaction forces were measured in the central 6 m of the walkway. Each participant performed three trials while walking barefoot and three trials while wearing shoes, each trial consisting of three 10 m walks. RESULTS: There were minimal differences between trials at each condition. All primary force, time, distance and velocity parameters had intraclass correlation coefficients above 0.90 and coefficients of variation in the order of 2% to 4%. Compared to walking barefoot, walking in shoes resulted in 14% lower maximal vertical ground reaction force, 5% longer step length and 2% higher average velocity and caused less lateral translation of the center of force. CONCLUSIONS: In this group of healthy adults, gait analysis with a force plate system produced repeatable intra-day results. The observation that barefoot and shod walking yield different results indicates that it is important to standardize test conditions.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.679
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.062
GPT teacher head0.218
Teacher spread0.156 · 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

Citations29
Published2011
Admission routes1
Has abstractyes

Explore more

Same venuePubMedSame topicLower Extremity Biomechanics and PathologiesFrench-language works237,207