Relationship between the cognitive status of the long-living adults of the Central Federal District of the Russian Federation and socioeconomic factors: analysis of associations
Bibliographic record
Abstract
Background: In recent years, an increase in the number of long-living adults has been a dominant demographic trend in Russia. This population group is highly susceptible to cognitive dysfunctions. Cognitive impairment disrupts the lives of those affected and puts an immense burden on their caregivers. Currently, there are no clinically applicable therapies or prevention strategies for cognitive impairment. Therefore, it is critically important to determine which lifestyle factors contribute to cognitive decline and to develop well-informed preventive strategies. The aim of the study: The study sought to identify the association between cognitive status and lifestyle. Materials and methods: The participants (n=2762) were recruited from 2019 to 2021 from the central regions of Russia. Detailed medical/case histories were obtained, including marital status, education, and social and economic background. Mini-Mental State Examination (MMSE) was used to evaluate cognitive status. The Mann-Whitney U test and Chi-squared test were used to test the associations between the sex and the factors under study. Logistic regression was used to assess the associations between the factors and cognitive impairment. Results: Age, sex, lower levels of education, and lower income were risk factors of cognitive impairment. Engaging in physical activity, hobbies, and having a pet were protective against cognitive impairment. In women, cognitive dysfunctions were correlated with the duration of menopause. The predictive model for cognitive dysfunctions based on sex, lifestyle and socioeconomic factors generated (ROC AUC=0.687). Conclusion: The findings confirmed that cognitive dysfunctions in long-living adults were associated with the socioeconomic factors, marital status, and lifestyle. The proposed model makes it possible to assess in advance the risk of developing cognitive impairments in old age and take measures to correct lifestyle to preserve brain functions.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".