The influence of TCM constitutions and neurocognitive function in elderly Macau individuals
Bibliographic record
Abstract
BACKGROUND: Traditional Chinese medicine (TCM) constitution contributes to predicating disease occurrence and pathological progress. In this study, we investigate the correlation between TCM constitution and neurocognitive function in elderly Macau individuals. METHODS: A total of 313 older adults from elderly healthcare centers were recruited at random. The data of gender, age, education, sleeping hours, physical activities were collected, and the Geriatric Depression Scale, Hong Kong version of the Montreal Cognitive Assessment (MoCA) and categories of TCM constitution were administered. RESULTS: Of the 313 elderly individuals enrolled in this study, 86 (27.48%) were of balanced constitution. Among the other categories of TCM constitution, the most was Yin-deficiency (23.32%), followed by 53 (16.93%) with Phlegm-dampness. The average neurocognitive score of all elderly individuals was 18.01 ± 6.25. After adjusting for all possible confounds, multiple linear regression analysis showed that Qi-depressed constitution and neurocognitive scores were negatively correlated (β = - 2.66, 95%CI - 4.99 ~ - 0.33), Meanwhile, Yin-deficient constitution and neurocognitive scores were negatively correlated (β = - 2.10, 95%CI - 3.73 ~ - 0.47). Compared with balanced constitution, Qi-depressed constitution mainly affected visual-spatial ability dimension (β = - 0.91, 95%CI - 1.54 ~ - 0.28) and naming dimension (β = - 0.64, 95%CI - 1.04 ~ - 0.25), Yin-deficient constitution mainly affected visual space dimension (β = - 0.53, 95%CI - 0.97 ~ - 0.08). CONCLUSION: Qi-depressed and Yin-deficient constitutions are associated with and contributed to the decline of neurocognitive function in senior adults, especially visual-spatial ability and naming dimensions. Further investigations into how TCM constitutions interact with neurocognitive function are needed.
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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 | 0.000 |
| 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".