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Record W1508084897 · doi:10.1111/jppi.12055

Hand Grip Strength is Associated With Body Sway Rate Among Older Adults With Intellectual Disability

2013· article· en· W1508084897 on OpenAlexaff
Eli Carmeli, Bita Imam, Ran Levi, Joav Merrick

Bibliographic record

VenueJournal of Policy and Practice in Intellectual Disabilities · 2013
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsGrip strengthLogistic regressionPhysical medicine and rehabilitationHand strengthPhysical therapyIntellectual disabilityMedicinePsychologyPsychiatry

Abstract

fetched live from OpenAlex

Abstract The authors undertook a study to determine whether hand grip strength is associated with body sway rate among older adults with intellectual disability. They employed cross‐sectional data from a nonrandomized controlled trial. Subjects were 16 older adults (8 females and 8 males; mean age 51.3 year) with mild‐to‐moderate intellectual disability residing in a supported living facility in I srael. Voluntary hand grip strength was measured isometrically using J amar® hydraulic dynamometer, and body sway was assessed by P osture S cale A nalyzer. Multivariate adjusted logistic regression model was used. Findings showed that hand grip strength in both arm flexed and extended was associated with body sway (−.608 to −.879), particularly among males. In females, the association was found only in eyes open condition, whereas in males it was found with both eyes closed and open. The authors concluded that hand grip strength was found to be negatively correlated with body sway rate and that low grip strength was associated with greater body sway. Low grip strength may be a rehabilitative impairment worthy of further investigation as a modifiable factor linked to sway rate among older adults with intellectual and developmental disability.

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)0.126
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.125
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.126
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.287
Teacher spread0.271 · 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.

Study designQualitative
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
Published2013
Admission routes1
Has abstractyes

Explore more

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