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Record W2919635000 · doi:10.5014/ajot.2019.029538

Handgrip Strength: A Comparison of Values Obtained From the NHANES and NIH Toolbox Studies

2019· article· en· W2919635000 on OpenAlexaff
Richard W. Bohannon, Ying-Chih Wang, Sheng‐Che Yen, Kimberly A. Grogan

Bibliographic record

VenueAmerican Journal of Occupational Therapy · 2019
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsKimberly-Clark (Canada)
Fundersnot available
KeywordsNational Health and Nutrition Examination SurveyGrip strengthNormativeToolboxMedicinePopulationPhysical therapyReference valuesGerontologyHand strengthPhysical medicine and rehabilitationEnvironmental healthInternal medicineComputer science

Abstract

fetched live from OpenAlex

IMPORTANCE: Handgrip dynamometry is probably the most commonly used method to characterize overall human muscle strength. OBJECTIVE: To compare and summarize grip strength measurements obtained from two population-based studies. DESIGN: Secondary data analysis. SETTING AND PARTICIPANTS: Data from (1) the 2011-2014 National Health and Nutrition Examination Survey (NHANES) with 13,918 participants and (2) the 2011 normative phase of the National Institutes of Health (NIH) Toolbox project with 3,594 participants. OUTCOMES AND MEASURES: The NHANES values used were the mean and best of three trials; the NIH Toolbox value used was the one maximum trial after a practice trial. RESULTS: General linear model analysis revealed that values obtained from the NIH Toolbox differed from NHANES best values but not from NHANES mean values. The analysis also indicated, regardless of the values used, that grip strength differed significantly between dominant and nondominant sides, males and females, and age groups. We provide updated reference values for handgrip strength. CONCLUSIONS AND RELEVANCE: On the basis of these analyses, we summarize grip strength measures obtained from the NHANES and NIH Toolbox for side, gender, and age group strata. Reference values are essential to assist in the interpretation of testing results and clinical decision making.

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)0.000
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 score0.062
Threshold uncertainty score0.206

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.147
GPT teacher head0.462
Teacher spread0.315 · 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

Citations76
Published2019
Admission routes1
Has abstractyes

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