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Record W2976980563 · doi:10.5216/ree.v21.52195

Prevalência e fatores associados à fragilidade em idosos atendidos em um ambulatório de especialidades

2019· article· pt· W2976980563 on OpenAlexaboutno aff
Clóris Regina Blanski Grden, Carla Regina Blanski Rodrigues, Luciane Patrícia Andreani Cabral, Péricles Martim Reche, Danielle Bordin, Pollyanna Kássia de Oliveira Borges

Bibliographic record

VenueRevista Eletrônica de Enfermagem · 2019
Typearticle
Languagept
FieldMedicine
TopicFrailty in Older Adults
Canadian institutionsnot available
Fundersnot available
KeywordsMedicine

Abstract

fetched live from OpenAlex


 
 
 
 Estudo transversal desenvolvido com 374 idosos de um hospital de ensino da região dos Campos Gerais que buscou identificar a prevalência e fatores associados à fragilidade em idosos de um ambulatório de especialidades médicas. A coleta de dados compreendeu entrevista, Mini Exame do Estado Mental e Escala de Fragilidade de Edmonton. Realizou-se análise bivariada e múltipla por meio de regressão de Poisson com os respectivos intervalos de confiança de 95% e nível de significância de p≤0,05. A prevalência de fragilidade foi de 40,1%, com associação significativa às variáveis sexo feminino (p=0,002), baixa escolaridade (p=0,020), presença de doença(s) autorreferida(s) (p=0,006), medicamentos (p=0,001), perda de urina (p=0,001), quedas (p=0,001) e à hospitalização (p=0,001). A prevalência de fragilidade identificada foi discretamente inferior à constatada em estudo de um centro de referência e superior a idosos da comunidade, com fatores sociodemográficos e clínicos associados. Requerendo olhar atento dos profissionais da saúde acerca deste perfil.
 
 
 

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)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.105
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.010

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 head0.023
GPT teacher head0.310
Teacher spread0.287 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2019
Admission routes1
Has abstractyes

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