Prevalence and factors associated with sarcopenia among Brazilian older adults: An exploratory network analysis
Bibliographic record
Abstract
OBJECTIVES: This study aimed to verify the prevalence of sarcopenia and its associations with sociodemographic, clinical and psychological factors in community-dwelling older adults. STUDY DESIGN: A randomized cross-sectional study was extracted from a probabilistic cluster conducted on individuals aged 65 years or older residing in the community. METHODS: Sarcopenia was defined according to the criteria of the European Working Group on Sarcopenia in Older People (EWGSOP2). Body composition was assessed using dual-energy X-ray absorptiometry (DXA). Associations were analyzed using networks based on mixed graphical models. Predictability indices of the estimated networks were assessed using the proportion of explained variance for numerical variables and the proportion of correct classification for categorical variables. RESULTS: The study included 278 participants, with a majority being female (61 %). The prevalence of sarcopenia was 39.57 %. Among those with sarcopenia, 67 % were women and 33 % were men. In the network model, age, race, education, family income, bone mass, depression, cardiovascular disease, diabetes, total cholesterol levels and rheumatism were associated with sarcopenia. The covariates demonstrated a high accuracy (62.9 %) in predicting sarcopenia categories. CONCLUSION: The prevalence of sarcopenia was high, especially in women. In addition, network analysis proved useful in visualizing complex relationships between sociodemographic and clinical factors with sarcopenia. The results suggest early screening of sarcopenia for appropriate treatment of this common geriatric syndrome in older adults in Brazil.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".