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Record W4402656312 · doi:10.15406/mojgg.2024.09.00326

Effect of frailty, activity of daily living, instrumental activity of daily living and age on cognition of community-dwelling elderly people: a cross-sectional study

2024· article· en· W4402656312 on OpenAlexaboutno aff
Marcos Eduardo Scheicher, Thainá Gabriele Camargo da Cruz, Livia Maria do Nascimento, Juliana Fernanda de Lima e Silva, Letícia Prado Silva, Beatriz Bigatão Inácio

Bibliographic record

VenueMOJ Gerontology & Geriatrics · 2024
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsActivities of daily livingGerontologyCognitionCross-sectional studyPsychologyAssisted livingMedicinePsychiatry

Abstract

fetched live from OpenAlex

Ageing is the main indicator of cognitive impairment and can affect activities of daily living (ADLs), instrumental activities of daily living (IADLs) and frailty. The objective of this study was to verify the interference of IADLs, ADLs, frailty status and age on the cognition of community-dwelling elderly people. Were used the follow tools: Montreal Cognitive Assessment (MoCa); Edmonton Frail Scale (EFS); Lawton scale and Barthel scale. All subjects included were community residents, aged 60 and over, both sexes. To evaluate the effect of age group and frailty status on cognition was used ANCOVA with Bonferroni post-test. Correlations were made using the Pearson test. Values of p <0.05 were accepted as significant. A relationship was observed between all independent variables and cognition (p <0.0001) and was observed an effect of age and frailty on cognition (p <0.001). There is an association between frailty, age, IADL and ADL with the cognition, and were found that the age group and frailty status interfere on cognitive impairment.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.038
GPT teacher head0.341
Teacher spread0.303 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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