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Postural control adjustments during progressive inclination of the support surface in children

2012· article· en· W2141598895 on OpenAlexaff
Mariève Blanchet, Denis Marchand, Geneviève Cadoret

Bibliographic record

VenueMedical Engineering & Physics · 2012
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPhysical medicine and rehabilitationForce platformBody weightElectromyographyVertical displacementSupport surfaceCentre of pressureMedicinePsychologyBalance (ability)EngineeringStructural engineering

Abstract

fetched live from OpenAlex

One of the most important postural challenges in daily life is to continuously correct the destabilizing torque due to gravity that accelerates the body further away from the upright position. This study examined children's (7.9 years old) (n=7) and adults' (n=10) capacity to generate continuous corrective torque during a progressive perturbation. The experimental task was to maintain an upright quiet standing on a platform that gradually and slowly toes-down tilted to a maximum of 14° without visual cues. The vertical forces applied on the platform and the electromyograms from the tibialis anterior and the gastrocnemius were measured. The results showed that children had a different postural response to the perturbation than adults. When the platform was stationary before the inclination, children shifted their body weight backward whereas adults had a more balanced distribution of their weight. During the inclination, children applied a stronger forward force, suggesting a larger postero-anterior displacement of their body weight. Muscular activities were higher in children for both the tibialis anterior and the gastrocnemius, and their tibialis anterior activation profile was different. In conclusion, this study showed that in children aged from 7 to 10 years old neuromuscular responses were not mature enough to generate continuous postural corrective torque in response to the perturbation.

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 score0.005
Threshold uncertainty score0.308

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 integrity0.0000.001
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 head0.007
GPT teacher head0.301
Teacher spread0.294 · 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

Citations7
Published2012
Admission routes1
Has abstractyes

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