Sociodemographic features associated with the MoCA, SPPB, and GDS scores in a community-dwelling elderly population
Bibliographic record
Abstract
BACKGROUND: An accurate evaluation of cognitive function, physical health, and psychological health is fundamental for assessing health problems in the elderly population, and it is important to identify the necessity of early therapeutic intervention. The objective of this study was to evaluate the states of mental and physical functions and to investigate the relationships between sociodemographic features and these functions in a community-dwelling elderly population. METHODS: This community-based cross-sectional study was conducted in a suburban district of Shanghai, China. A total of 1025 participants aged 60-89 years underwent investigations of demographic and lifestyle features and a multidimensional geriatric evaluation comprising the Montreal Cognitive Assessment (MoCA), Short Physical Performance Battery (SPPB), and Geriatric Depression Scale (GDS). RESULTS: The results of the multivariate linear regression models demonstrated that the MoCA and SPPB scores decreased with advancing age (all P < 0.01). However, the GDS score did not exhibit an age-related decrease (P = 0.09). Both sex and living alone influenced the MoCA score (P < 0.01 and P = 0.04, respectively), SPPB score (P < 0.01 and P = 0.04, respectively), and GDS score (P < 0.01 and P < 0.01, respectively). A higher education level was related to better MoCA and SPPB scores (all P < 0.01). Furthermore, age and sex had interactive effects on the MoCA score (P = 0.03) and SPPB score (P < 0.01). The kernel-weighted local polynomial smoothing curves exhibited similar trends. CONCLUSIONS: It is imperative to develop a more sensitive evaluation of physical function, and to encourage various intellectually and emotionally stimulating social activity strategies to promote healthy aging, especially in elderly women and those living alone who have a low education level.
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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 | 0.000 | 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".