Factors influencing cognitive function in Chinese elderly individuals: The role of traditional Chinese medicine in a large-scale cross-sectional study
Bibliographic record
Abstract
To identify key factors influencing cognitive function in the elderly, including Traditional Chinese medicine (TCM) constitutional classification, and to rank their relative importance. We used cross-sectional data from seven geographical regions across mainland China. The Changsha version of the Montreal Cognitive Assessment was used to assess cognitive function. A “least absolute shrinkage and selection operator” (LASSO) model, multivariate linear regression analysis, and random forest (RF) model were used. Subgroup analyses were performed to examine the correlation between key TCM constitution types and cognitive function in different population subgroups. A total of 24 803 individuals aged 60 and above were included in the study. We selected 18 influential factors using the LASSO model. Higher education, being married, and having insurance were positively correlated with cognitive function in the elderly (all P < .05). In contrast, poor sleep, vision impairment, hearing impairment, basic activities of daily living disability, instrumental activities of daily living disability, depression, hypertension, coronary heart disease, diabetes, stroke, yang-deficiency constitution (YADC), yin-deficiency constitution (YIDC), qi deficiency constitution (QDC), and blood stasis constitution (BSC) were negatively correlated with cognitive function (all P < .05). YIDC and BSC affected all dimensions of cognitive function (all P < .05). YADC mainly affected attention, language, abstraction (verbal analogies), memory, and orientation to time and place dimensions ( P < .001). QDC mainly affected language and abstraction (verbal analogies) dimensions ( P < .05). The negative correlations between BSC, YADC, YIDC, and QDC scores and cognitive function revealed statistically significant differences across most subgroups. The RF model identified education, BSC, and poor sleep quality as the three most influential factors in our study. BSC, YADC, YIDC, and QDC were associated with cognitive decline in the elderly. Our findings provide new perspectives and significant references for interventions for early-stage cognitive disorders.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".