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Factors associated with frailty in older adults

2018· article· en· W2886780381 on OpenAlexaboutno aff
Jack Roberto Silva Fhon, Rosalina Aparecida Partezani Rodrigues, Jair Lício Ferreira Santos, Marina Aleixo Diniz, Emanuella Barros dos Santos, Vanessa Costa Almeida, Suelen Borelli Lima Giacomini

Bibliographic record

VenueRevista de Saúde Pública · 2018
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
FundersCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsGerontologyMarital statusMedicineDescriptive statisticsActivities of daily livingGeneralized estimating equationWilcoxon signed-rank testGeeScale (ratio)DemographyPopulationEnvironmental healthPhysical therapyStatistics

Abstract

fetched live from OpenAlex

OBJECTIVE: To determine the demographic and health factors related to the frailty syndrome in older adults. METHODS: This is a longitudinal quantitative study carried out with 262 older adults aged 65 years and older, of both sexes, living at home. Data collection was carried out in Period 1 between October 2007 and February 2008, and in Period 2 between July and December 2013. For data collection, we used the sociodemographic profile instrument, the Edmonton Frail Scale, the Mini-Mental State Examination, the number of falls in the last 12 months, the number of self-reported diseases and used drugs, the Functional Independence Measure, and the Lawton and Brody Scale. We used descriptive statistics for data analysis, in the comparison of the means between periods, the nonparametric Wilcoxon test, and the method of Generalized Estimating Equations, which is considered an extension of the Generalized Linear Models with p ≤ 0.05. RESULTS: Of the 515 participants, 262 completed the follow-up, with a predominance of females, older individuals, and those who had no partner; there was an increase in frail older adults. In the Generalized Estimating Equations analysis, frailty score was related to sociodemographic (increase in age, no partner, and low education level) and health variables (more diseases, drugs, falls, and decrease in functional capacity). There was an association between the variables of age (older), marital status (no partner), and loss of functional capacity. CONCLUSIONS: Frailty syndrome was associated with increasing age, having no partner, and decreased functional capacity over time, and investments are required to prevent this syndrome and promote quality in aging.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.946

Codex and Gemma teacher scores by category

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

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 head0.034
GPT teacher head0.294
Teacher spread0.261 · 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

Citations105
Published2018
Admission routes1
Has abstractyes

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