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Record W2684018374 · doi:10.3389/fnagi.2017.00202

Loss of Peripheral Sensory Function Explains Much of the Increase in Postural Sway in Healthy Older Adults

2017· article· en· W2684018374 on OpenAlexaff
Eric R. Anson, Robin T. Bigelow, Bonnielin K. Swenor, Nandini Deshpande, Stephanie A. Studenski, John J. Jeka, Yuri Agrawal

Bibliographic record

VenueFrontiers in Aging Neuroscience · 2017
Typearticle
Languageen
FieldNeuroscience
TopicVestibular and auditory disorders
Canadian institutionsQueen's University
FundersNational Institute on Deafness and Other Communication DisordersNational Institutes of Health
KeywordsSensory systemProprioceptionVestibular systemBalance (ability)PeripheralPosturographyAudiologyPhysical medicine and rehabilitationSensory lossMedicineYoung adultPsychologyNeuroscienceInternal medicine

Abstract

fetched live from OpenAlex

Postural sway increases with age and peripheral sensory disease. Whether peripheral sensory function is related to postural sway independent of age in healthy adults is unclear. Here we investigated the relationship between tests of visual function (VISFIELD), vestibular function (CANAL or OTOLITH), proprioceptive function (PROP), and age, with center of mass sway area (COM) measured with eyes open then closed on firm and then a foam surface. A cross-sectional sample of 366 community dwelling healthy adults from the Baltimore Longitudinal Study of Aging was tested. Multiple linear regressions examined the association between COM and VISFIELD, PROP, CANAL, and OTOLITH separately and in multi-sensory models controlling for age and gender. PROP dominated sensory prediction of sway across most balance conditions (β’s = 0.09-0.19, p’s < 0.001), except on foam eyes closed where CANAL function loss was the only significant sensory predictor of sway (β = 2.12, p < 0.016). Age was not a consistent predictor of sway. This suggests loss of peripheral sensory function explains much of the age-associated increase in sway.

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)0.001
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 score0.041
Threshold uncertainty score0.534

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.016
GPT teacher head0.259
Teacher spread0.242 · 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

Citations74
Published2017
Admission routes1
Has abstractyes

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