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Association between physical frailty and cognitive scores in older adults

2015· article· en· W4240627344 on OpenAlexaboutno aff
Clóris Regina Blanski Grden, Maynara Fernanda Carvalho Barreto, Jacy Aurélia Vieira de Sousa, Juliana Andrade Chuertniek, Péricles Martim Reche, Pollyanna Kássia de Oliveira Borges

Bibliographic record

VenueRev Rene · 2015
Typearticle
Languageen
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsGerontologyCognitionAssociation (psychology)Cognitive impairmentMedicineCognitive declineFrailty IndexPsychologyDementiaPsychiatryDisease

Abstract

fetched live from OpenAlex

Objective: to investigate the association between physical frailty and cognitive scores in older adults at an Open University of the Third Age in Southern Brazil. Methods: descriptive cross-sectional study with convenience sample comprising 100 elderly, conducted from March to June 2013. For cognitive assessment, we applied the Mini Mental State Examination and the Edmonton Frail Scale. Results: there was a predominance of females (93%), with a mean age of 65.6 years. 81% of the participants were classified as non-frail, 16% as apparently vulnerable to frailty, and 3% as mild frailty. There was a significant association between cognitive performance and frailty (p<0.006). Conclusion: the research on the association between physical frailty and cognitive scores in older people promotes the construction of gerontological care plans aimed at managing this syndrome.

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 score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.033
GPT teacher head0.317
Teacher spread0.284 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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