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Record W4389317152 · doi:10.1590/0034-7167-2022-0677pt

Avaliação dos componentes da sarcopenia e qualidade de vida percebida de indivíduos em hemodiálise

2023· article· pt· W4389317152 on OpenAlexaff
Bianca Raquel Bianchi Celoto, Flávia Andréia Marin, Maria Cláudia Bernardes Spexoto

Bibliographic record

VenueRevista Brasileira de Enfermagem · 2023
Typearticle
Languagept
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsSarcopeniaMedicineMuscle strengthMuscle massGerontologyInternal medicine

Abstract

fetched live from OpenAlex

RESUMO Objetivos: avaliar a prevalência de sarcopenia em indivíduos com 50 anos ou mais em hemodiálise, verificar a associação entre a sarcopenia e os fatores sociodemográficos, clínicos, antropométricos, componentes da sarcopenia e qualidade de vida (QV), e correlacionar os componentes da sarcopenia com a QV. Métodos: Participaram 83 indivíduos em hemodiálise. A sarcopenia foi estabelecida segundo consenso europeu vigente. A dinamometria para determinação da força, a circunferência da panturrilha (CP) e o índice de massa muscular esquelética apendicular (IMMEA) para a obtenção da massa muscular e a velocidade de caminhada (VC) para o desempenho físico. Para QV utilizou-se WHOQOL-bref. Resultados: a prevalência de sarcopenia foi de 32,6% (CP) e 18,1% (IMMEA). Não houve associação entre a sarcopenia e QV. Tanto a força de preensão manual (r=0,25) quanto a VC (r=0,36) apresentaram correlação com domínio físico. Conclusões: a sarcopenia foi expressiva e os aspectos da funcionalidade determinam o comprometimento físico nessa população.

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 score0.002
Threshold uncertainty score0.005

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 communication0.0010.000
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 head0.117
GPT teacher head0.398
Teacher spread0.281 · 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
Published2023
Admission routes1
Has abstractyes

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