Prevalence of sarcopenia in socially active older adults and its association with sex and nutritional risk: an analysis of Bayesian networks
Bibliographic record
Abstract
Abstract Objective To verify the prevalence of sarcopenia and its relationship with sociodemographic and clinical factors, including nutritional risk, in socially active older adults, as well as to investigate sex differences in factors associated with sarcopenia. Method A cross-sectional study included 400 older adults (342 women, 58 men) attending community groups in the city of Santa Maria, RS, Brazil. Participants were diagnosed according to EWGSOP2 (2019) criteria, and nutritional status was assessed using the Mini Nutritional Assessment (MNA®). Bayesian network models with total sample and stratified by sex were used to explore the relationship between sarcopenia and sociodemographic and clinical factors. A resampling process was conducted to assess the statistical performance of the network models. Results The prevalence of sarcopenia was 10.2%. In the network model for the total sample, the probability of sarcopenia was higher among men with nutritional risk. In the female network, the probability of being sarcopenic was higher in the subgroup of long-lived individuals aged 80 years or older. In contrast, in the male network only nutritional risk increased the probability of sarcopenia. All network models reached good performance, with an area under the ROC curve (AUC) above 0.89. Conclusion This study was the first to use Bayesian networks to investigate factors associated with sarcopenia in socially active older adults, which differ by sex. This study highlights the importance of diagnosing sarcopenia and incorporating MNA® in clinical practice.
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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 | 0.016 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".