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Record W3199237749 · doi:10.1159/000519054

Frailty and Its Correlates in Cognitively Intact Community-Dwelling Older Adults

2021· article· en· W3199237749 on OpenAlexaboutno aff
Audai A. Hayajneh, Hanan Hammouri, Mohammad Rababa, Sami Al‐Rawashdeh, Debra C. Wallace, Eman S. Alsatari

Bibliographic record

VenueDementia and Geriatric Cognitive Disorders · 2021
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsDepression (economics)GerontologyQuality of life (healthcare)DementiaPsychologyCognitionGeriatric Depression ScaleActivities of daily livingMedicinePsychiatryDiseaseDepressive symptoms

Abstract

fetched live from OpenAlex

BACKGROUND: Frailty syndrome is characterized by a decline in physiological and psychological reserve and may be associated with poor health outcomes. OBJECTIVES: The current study explored frailty and its correlates among cognitively intact community-dwelling older adults. METHODS: A secondary analysis of data collected from 109 community-dwelling older adults who are cognitively intact was conducted for the purpose of this study. The Arabic versions of the culturally adapted Tilburg Frailty Indicator, the Montreal Cognitive Assessment, the Geriatric Depression Scale, and the Short Form-36 Quality of Life (QOL) survey. Multiple linear regression was used to examine the relationships between frailty and depression. RESULTS: The results indicated a high prevalence of frailty (78%) and depression (38%) among cognitively intact community-dwelling older adults. Frailty was found to be associated with increased age, being single or illiterate, living alone, having a high number of comorbid conditions, having high rate of depression, and having poor QOL. CONCLUSION: High prevalence of frailty is associated with high depression scores, a high number of comorbid conditions, and poor QOL among cognitively intact community-dwelling older adults.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.269
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.266
Teacher spread0.253 · 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 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

Citations7
Published2021
Admission routes1
Has abstractyes

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