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Record W2940827847 · doi:10.12965/jer.1938026.013

Effects of gaze stability exercises on cognitive function, dynamic postural ability, balance confidence, and subjective health status in old people with mild cognitive impairment

2019· article· en· W2940827847 on OpenAlexaboutno aff
Miyoung Roh, Eunja Lee

Bibliographic record

VenueJournal of Exercise Rehabilitation · 2019
Typearticle
Languageen
FieldMedicine
TopicStroke Rehabilitation and Recovery
Canadian institutionsnot available
FundersNational Research Foundation of KoreaNational Research Foundation
KeywordsBalance (ability)CognitionPhysical medicine and rehabilitationGazeCognitive impairmentDynamic balancePsychologyMedicineNeuroscienceEngineering

Abstract

fetched live from OpenAlex

The purpose of this study was to determine whether gaze stability exercises would result in improvements of cognitive function, balance ability and subjective health status in old people with and without mild cognitive impairment (MCI). Old people with MCI (n=9) and healthy old people (n=9) performed gaze stability exercises for 4 weeks. Pre and post Montreal Cognitive Assessment (MoCA) for cognitive function, Timed Up and Go test for dynamic postural ability, Activities-Specific Balance Confidence for balance confidence and subjective health status were measured in both groups. After participating in gaze stability exercises, all outcome measures were significantly improved in MCI group and normal group also improved significantly in all outcome measures with the exception of balance confidence. In addition, there were significant differences in cognitive function and balance confidence between the two groups, and more improvements in MCI group. These results provide evidence that gaze stability exercises is beneficial to improve cognitive function as well as balance ability which affected on quality of life in old people with and without MCI.

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)0.002
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 score0.021
Threshold uncertainty score0.885

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.005
GPT teacher head0.263
Teacher spread0.259 · 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

Citations17
Published2019
Admission routes1
Has abstractyes

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