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Record W2137231120 · doi:10.1123/mc.2013-0091

Unsupported Eyes Closed Sitting and Quiet Standing Share Postural Control Strategies in Healthy Individuals

2014· article· en· W2137231120 on OpenAlexaff
Murielle Grangeon, Cindy Gauthier, Cyril Duclos, Jean-François Lemay, Dany H. Gagnon

Bibliographic record

VenueMotor Control · 2014
Typearticle
Languageen
FieldHealth Professions
TopicBalance, Gait, and Falls Prevention
Canadian institutionsUniversité de MontréalCentre for Interdisciplinary Research in Rehabilitation
Fundersnot available
KeywordsSittingEyes openCenter of pressure (fluid mechanics)Physical medicine and rehabilitationQUIETBalance (ability)Force platformPhysical therapyPsychologyMedicinePhysicsMechanics

Abstract

fetched live from OpenAlex

The study aimed to (1) compare postural stability between sitting and standing in healthy individuals and (2) define center-of-pressure (COP) measures during sitting that could also explain standing stability. Fourteen healthy individuals randomly maintained (1) two short-sitting positions with eyes open or closed, with or without hand support, and (2) one standing position with eyes open with both upper limbs resting alongside the body. Thirty-six COP measures based on time and frequency series were computed. Greater COP displacement and velocity along with lower frequency measures were found for almost all directional components during standing compared with both sitting positions. The velocity, 95% confidence ellipse area, and centroidal frequency were found to be correlated between unsupported sitting and standing. Despite evidenced differences between sitting and standing, similarities in postural control were highlighted when sitting stability was the most challenging. These findings support further investigation between dynamic sitting and standing balance.

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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.339
Teacher spread0.319 · 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

Citations15
Published2014
Admission routes1
Has abstractyes

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