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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 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 score0.027
Threshold uncertainty score0.054

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 studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
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; 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

Citations5
Published2019
Admission routes1
Has abstractyes

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