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Record W7120809118

Prevalence of sarcopenic obesity in brazilian elderly people: a systematic review with methanalysis

2019· dissertation· pt· W7120809118 on OpenAlexaboutno aff
Adriane Rosa Costodio

Bibliographic record

VenueLA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) · 2019
Typedissertation
Languagept
FieldMedicine
TopicNutrition and Health in Aging
Canadian institutionsnot available
Fundersnot available
KeywordsSarcopenic obesitySarcopeniaObesitySciELOMuscle massQuality of life (healthcare)PrevalenceScale (ratio)
DOInot available

Abstract

fetched live from OpenAlex

The interest in the study of aging has been growing due to the increase in the number of elderly and also because they are independent. During the aging process, there are several changes, including those related to body composition, which may cause an increase in adipose and visceral tissue leading to the onset of obesity and may also muscle mass reduction and strength, contributing to the development of sarcopenia. The simultaneous occurrence of sarcopenia and obesity in the elderly, the condition called sarcopenic obesity may develop which can be associated with health problems and decreased quality of life. The present study aimed to determine the prevalence of sarcopenic obesity in Brazilian elderly. This is a systematic review with meta-analysis. The databases PubMed, LILACS, Scopus, Scielo were consulted. As inclusion criteria we selected articles that investigated sarcopenic obesity, published in Portuguese, English and Spanish from 2010, involving Brazilian elderly aged 60 years and over of both sexes. Dissertations, theses, experimental animal studies, in vitro studies, recommendations, guidelines, reviews, protocols, letters, editorials, case reports, case series and duplicates were excluded. We used the descriptors, sarcopenic obesity, elderly, Brazilians and Brazil and their correlates in English and Spanish. In order to evaluate the quality of the studies, the Newcastle Ottawa Scale was used. Statistical analysis was performed using R Studio 3.6.0 software, heterogeneity through I² statistics and presented in forest plot graph. The total of 12 studies were included in this study. The prevalence of Brazilian elderly with sarcopenic obesity was 15% (95% CI: 10-23%), with the highest prevalence found in the Midwest. A high heterogeneity was found in the general prevalence analyzes and subgroups (regions and criteria and diagnostic methods). A sensitivity analysis was performed omitting the extreme prevalence values found in the included studies, where a 3% reduction in the overall prevalence of sarcopenic obesity was observed. In the Midwest regions and in the criteria and diagnostic methods there was also a reduction in their prevalence and heterogeneity. In methods that used an equation for diagnosis of sarcopenic obesity, heterogeneity reached 0%, indicating a low heterogeneity. It is concluded that a 15% prevalence of sarcopenic obesity was found in the Brazilian elderly. According to this study, it was possible to present important information on the conceptualization, epidemiology and how sarcopenic obesity can be diagnosed in the elderly. The criteria and methods used to diagnose sarcopenic obesity are different among studies regarding assessment, cutoff points and definitions, and may lead to different prevalences of sarcopence obesity, thus making it difficult to manage preventive measures of this condition, especially in the elderly population thus can interfere with the quality of life of this population.

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)0.007
metaresearch head score (Gemma)0.026
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.014
Threshold uncertainty score0.040

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.026
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0090.011
Bibliometrics0.0140.018
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.021
GPT teacher head0.292
Teacher spread0.271 · 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 designMeta-analysis
Domainnot available
GenreReview

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
Published2019
Admission routes1
Has abstractyes

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