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Record W2000159512 · doi:10.1123/mc.2014-0046

Sensory Interactions for Head and Trunk Control in Space in Young and Older Adults During Normal and Narrow-Base Walking

2015· article· en· W2000159512 on OpenAlexaff
Fang Zhang, Nandini Deshpande

Bibliographic record

VenueMotor Control · 2015
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsQueen's University
Fundersnot available
KeywordsTrunkGalvanic vestibular stimulationVestibular systemTorsoPhysical medicine and rehabilitationGaitSensory systemMedicineHead (geology)AudiologyPsychologyAnatomyNeuroscience

Abstract

fetched live from OpenAlex

Fifteen young (20-30 years old) and 15 older (>65 years old) healthy participants were recruited to investigate age-related differences in head and trunk control under suboptimal vestibular conditions (galvanic vestibular stimulation, or GVS) and vision conditions during normal and narrow-based walking. Head-roll velocity decreased in the blurred-vision condition and marginally increased with GVS in older but not in young participants. Head pitch increased, whereas head-roll velocity decreased in narrow-base walking. Trunk pitch, trunk-pitch velocity, and gait speed increased with GVS, whereas trunk-pitch velocity and gait speed decreased in narrow-base walking. Marginally increased head-roll velocity in the older participants possibly suggests decreased integrative ability of the central nervous system in elderly people. The changes in head control during narrow-base walking may be an attempt to simplify the interpretation of the vestibular signal and increase otolith sensitivity. The complexity of controlling the trunk in the mediolateral direction was suggested by different strategies used for trunk control in different 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 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 score0.003
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)0.0010.000

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 head0.013
GPT teacher head0.247
Teacher spread0.234 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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