Prevalência de declínio neurocognitivo leve e vulnerabilidade clínico-funcional em população idosa rural no norte do estado do Rio Grande do Sul
Bibliographic record
Abstract
Increased life expectancy poses health challenges, directing effective strategies for aging. This process involves social, psychological, physiological and cognitive changes, favoring the emergence of Mild Cognitive Decline and Clinical-Functional Vulnerability, which impact the quality of life of the elderly population. In addition to age, modifiable factors, such as living in rural areas, level of education, access to health care and presence of chronic diseases directly influence healthy aging, and can act as both risk factors and protective elements. This dissertation investigated the prevalence of MCI and Clinical-Functional Vulnerability in elderly people living in rural areas in northern Rio Grande do Sul, aiming to support health strategies and policies. This is a cross-sectional quantitative observational study that evaluated 435 elderly people living in rural areas. The Montreal Cognitive Basic (MoCA-B), the Clinical-Functional Vulnerability Instrument - 20 (IVCF-20) and a Sociodemographic Questionnaire were used. All participants answered the instruments at home. Most participants were women (64.5%), aged 60 to 80 years, with low levels of education and dependency on rural work. Access to health care was mainly through the SUS (80.5%). MCI was identified in 38.86% of the elderly, while 61.14% had preserved cognition. Regarding Clinical-Functional Vulnerability, 84.7% exhibited low vulnerability, 9.5% were at risk of frailty and 3.8% presented frailty. There was an association between age and education with MCI, but no relationship was found between chronic diseases and MCI. The results highlight the importance of quality access to public health care in strengthening the health conditions of the elderly, contributing to mitigating the effects of environmental risk factors.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".