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Record W2514044339 · doi:10.1080/21551197.2016.1209146

Evaluation of Handgrip Strength and Nutritional Risk of Congregate Nutrition Program Participants in Florida

2016· article· en· W2514044339 on OpenAlexaff
Kelly A. Springstroh, Nancy J. Gal, Amanda Ford, Susan J. Whiting, Wendy J. Dahl

Bibliographic record

VenueJournal of Nutrition in Gerontology and Geriatrics · 2016
Typearticle
Languageen
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMedicineMalnutritionGerontologyHand strengthPopulationRisk assessmentPhysical therapyEnvironmental healthCross-sectional studyGrip strengthInternal medicine

Abstract

fetched live from OpenAlex

The aim of this study was to determine if handgrip strength (HGS) is a predictor of nutritional risk in community-dwelling older adults. A cross-sectional study was carried out to determine the relationship between HGS and nutritional risk using SCREEN 1. The setting was Congregate Nutrition program meal sites (n = 10) in North Central Florida and included community-dwelling older adults participating in the Congregate Nutrition program. Older adults (n = 136; 77.1 ± 8.9 y; 45 M, 91 F) participated in the study. Nutritional risk was identified in 68% of participants, with 10% exhibiting clinically relevant weakness (men, HGS < 26 kg; women, HGS < 16 kg), suggesting a vulnerable population. HGS was weakly associated with nutritional risk as assessed by SCREEN 1 (AUC = 0.59), but alternate cutpoints, 33 kg for men (mean of both hands) and 22 kg for women (highest of either hand), provided the best comparison to nutritional risk. In community-dwelling older adults, HGS was weakly associated with nutritional risk assessed using traditional screening. However, as existing research supports the inclusion of HGS in malnutrition screening in acute care, further research into the usefulness of HGS and possibly other measures of functional status in nutrition risk screening of community-dwelling older adults may be warranted.

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)0.003
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 score0.136
Threshold uncertainty score0.378

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.082
GPT teacher head0.387
Teacher spread0.305 · 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

Citations20
Published2016
Admission routes1
Has abstractyes

Explore more

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